Update YOLO11 Actions and Docs (#16596)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -56,7 +56,7 @@ body:
       placeholder: |
         Paste output of `yolo checks` or `ultralytics.checks()` command, i.e.:
         ```
-        Ultralytics YOLOv8.0.181 🚀 Python-3.11.2 torch-2.0.1 CPU (Apple M2)
+        Ultralytics 8.3.2 🚀 Python-3.11.2 torch-2.4.1 CPU (Apple M3)
         Setup complete ✅ (8 CPUs, 16.0 GB RAM, 266.5/460.4 GB disk)
 
         OS                  macOS-13.5.2
@@ -64,7 +64,7 @@ body:
         Python              3.11.2
         Install             git
         RAM                 16.00 GB
-        CPU                 Apple M2
+        CPU                 Apple M3
         CUDA                None
         ```
     validations:

.github/ISSUE_TEMPLATE/config.yml

@@ -4,7 +4,7 @@ blank_issues_enabled: true
 contact_links:
   - name: 📄 Docs
     url: https://docs.ultralytics.com/
-    about: Full Ultralytics YOLOv8 Documentation
+    about: Full Ultralytics YOLO Documentation
   - name: 💬 Forum
     url: https://community.ultralytics.com/
     about: Ask on Ultralytics Community Forum

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,14 +1,14 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 name: 🚀 Feature Request
-description: Suggest a YOLOv8 idea
+description: Suggest an Ultralytics YOLO idea
 # title: " "
 labels: [enhancement]
 body:
   - type: markdown
     attributes:
       value: |
-        Thank you for submitting a YOLOv8 🚀 Feature Request!
+        Thank you for submitting an Ultralytics 🚀 Feature Request!
 
   - type: checkboxes
     attributes:
@@ -17,7 +17,7 @@ body:
         Please search the Ultralytics [Docs](https://docs.ultralytics.com) and [issues](https://github.com/ultralytics/ultralytics/issues) to see if a similar feature request already exists.
       options:
         - label: >
-            I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar feature requests.
+            I have searched the Ultralytics [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar feature requests.
           required: true
 
   - type: textarea
@@ -25,7 +25,7 @@ body:
       label: Description
       description: A short description of your feature.
       placeholder: |
-        What new feature would you like to see in YOLOv8?
+        What new feature would you like to see in YOLO?
     validations:
       required: true
 
@@ -46,7 +46,7 @@ body:
     attributes:
       label: Are you willing to submit a PR?
       description: >
-        (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/ultralytics/pulls) (PR) to help improve YOLOv8 for everyone, especially if you have a good understanding of how to implement a fix or feature.
-        See the YOLOv8 [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started.
+        (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/ultralytics/pulls) (PR) to help improve YOLO for everyone, especially if you have a good understanding of how to implement a fix or feature.
+        See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started.
       options:
         - label: Yes I'd like to help by submitting a PR!

.github/workflows/ci.yaml

@@ -320,16 +320,16 @@ jobs:
           conda list
       - name: Test CLI
         run: |
-          yolo predict model=yolov8n.pt imgsz=320
-          yolo train model=yolov8n.pt data=coco8.yaml epochs=1 imgsz=32
-          yolo val model=yolov8n.pt data=coco8.yaml imgsz=32
-          yolo export model=yolov8n.pt format=torchscript imgsz=160
+          yolo predict model=yolo11n.pt imgsz=320
+          yolo train model=yolo11n.pt data=coco8.yaml epochs=1 imgsz=32
+          yolo val model=yolo11n.pt data=coco8.yaml imgsz=32
+          yolo export model=yolo11n.pt format=torchscript imgsz=160
       - name: Test Python
         # Note this step must use the updated default bash environment, not a python environment
         run: |
           python -c "
           from ultralytics import YOLO
-          model = YOLO('yolov8n.pt')
+          model = YOLO('yolo11n.pt')
           results = model.train(data='coco8.yaml', epochs=3, imgsz=160)
           results = model.val(imgsz=160)
           results = model.predict(imgsz=160)

.github/workflows/format.yml

@@ -48,7 +48,7 @@ jobs:
 
             ## Environments
 
-            YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
+            YOLO may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
 
             - **Notebooks** with free GPU: <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a> <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
             - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
@@ -59,4 +59,4 @@ jobs:
 
             <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml?query=event%3Aschedule"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
 
-            If this badge is green, all [Ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml?query=event%3Aschedule) tests are currently passing. CI tests verify correct operation of all YOLOv8 [Modes](https://docs.ultralytics.com/modes/) and [Tasks](https://docs.ultralytics.com/tasks/) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
+            If this badge is green, all [Ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml?query=event%3Aschedule) tests are currently passing. CI tests verify correct operation of all YOLO [Modes](https://docs.ultralytics.com/modes/) and [Tasks](https://docs.ultralytics.com/tasks/) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

docs/en/datasets/classify/caltech101.md

@@ -36,7 +36,7 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="caltech101", epochs=100, imgsz=416)
@@ -46,7 +46,7 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
         ```
 
 ## Sample Images and Annotations
@@ -98,7 +98,7 @@ To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the p
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="caltech101", epochs=100, imgsz=416)
@@ -108,7 +108,7 @@ To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the p
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
         ```
 
 For more detailed arguments and options, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/classify/caltech256.md

@@ -47,7 +47,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="caltech256", epochs=100, imgsz=416)
@@ -57,7 +57,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=caltech256 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech256 model=yolo11n-cls.pt epochs=100 imgsz=416
         ```
 
 ## Sample Images and Annotations
@@ -106,7 +106,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ul
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model
 
         # Train the model
         results = model.train(data="caltech256", epochs=100, imgsz=416)
@@ -116,7 +116,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ul
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=caltech256 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech256 model=yolo11n-cls.pt epochs=100 imgsz=416
         ```
 
 ### What are the most common use cases for the Caltech-256 dataset?
@@ -141,6 +141,6 @@ Ultralytics YOLO models offer several advantages for training on the Caltech-256
 - **High Accuracy**: YOLO models are known for their state-of-the-art performance in object detection tasks.
 - **Speed**: They provide real-time inference capabilities, making them suitable for applications requiring quick predictions.
 - **Ease of Use**: With Ultralytics HUB, users can train, validate, and deploy models without extensive coding.
-- **Pretrained Models**: Starting from pretrained models, like `yolov8n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
+- **Pretrained Models**: Starting from pretrained models, like `yolo11n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
 
 For more details, explore our [comprehensive training guide](../../modes/train.md).

docs/en/datasets/classify/cifar10.md

@@ -16,7 +16,7 @@ The [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to Train an <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model with CIFAR-10 Dataset using Ultralytics YOLOv8
+  <strong>Watch:</strong> How to Train an <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model with CIFAR-10 Dataset using Ultralytics YOLO11
 </p>
 
 ## Key Features
@@ -50,7 +50,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="cifar10", epochs=100, imgsz=32)
@@ -60,7 +60,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar10 model=yolo11n-cls.pt epochs=100 imgsz=32
         ```
 
 ## Sample Images and Annotations
@@ -104,7 +104,7 @@ To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="cifar10", epochs=100, imgsz=32)
@@ -114,7 +114,7 @@ To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar10 model=yolo11n-cls.pt epochs=100 imgsz=32
         ```
 
 For more details, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/classify/cifar100.md

@@ -39,7 +39,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 [epochs](https://www.ultr
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="cifar100", epochs=100, imgsz=32)
@@ -49,7 +49,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 [epochs](https://www.ultr
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
         ```
 
 ## Sample Images and Annotations
@@ -97,7 +97,7 @@ You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI c
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="cifar100", epochs=100, imgsz=32)
@@ -107,7 +107,7 @@ You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI c
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
         ```
 
 For a comprehensive list of available arguments, please refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/classify/fashion-mnist.md

@@ -16,7 +16,7 @@ The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to do <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> on Fashion MNIST Dataset using Ultralytics YOLOv8
+  <strong>Watch:</strong> How to do <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> on Fashion MNIST Dataset using Ultralytics YOLO11
 </p>
 
 ## Key Features
@@ -64,7 +64,7 @@ To train a CNN model on the Fashion-MNIST dataset for 100 [epochs](https://www.u
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
@@ -74,7 +74,7 @@ To train a CNN model on the Fashion-MNIST dataset for 100 [epochs](https://www.u
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=fashion-mnist model=yolo11n-cls.pt epochs=100 imgsz=28
         ```
 
 ## Sample Images and Annotations
@@ -107,7 +107,7 @@ To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use bot
         from ultralytics import YOLO
 
         # Load a pretrained model
-        model = YOLO("yolov8n-cls.pt")
+        model = YOLO("yolo11n-cls.pt")
 
         # Train the model on Fashion-MNIST
         results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
@@ -117,7 +117,7 @@ To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use bot
     === "CLI"
 
         ```bash
-        yolo classify train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=fashion-mnist model=yolo11n-cls.pt epochs=100 imgsz=28
         ```
 
 For more detailed training parameters, refer to the [Training page](../../modes/train.md).
@@ -128,7 +128,7 @@ The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is
 
 ### Can I use Ultralytics YOLO for image classification tasks like Fashion-MNIST?
 
-Yes, Ultralytics YOLO models can be used for image classification tasks, including those involving the Fashion-MNIST dataset. YOLOv8, for example, supports various vision tasks such as detection, segmentation, and classification. To get started with image classification tasks, refer to the [Classification page](https://docs.ultralytics.com/tasks/classify/).
+Yes, Ultralytics YOLO models can be used for image classification tasks, including those involving the Fashion-MNIST dataset. YOLO11, for example, supports various vision tasks such as detection, segmentation, and classification. To get started with image classification tasks, refer to the [Classification page](https://docs.ultralytics.com/tasks/classify/).
 
 ### What are the key features and structure of the Fashion-MNIST dataset?
 

docs/en/datasets/classify/imagenet.md

@@ -12,11 +12,11 @@ keywords: ImageNet, deep learning, visual recognition, computer vision, pretrain
 
 | Model                                                                                        | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
 | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
-| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-cls.pt) | 224                   | 69.0             | 88.3             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
-| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-cls.pt) | 224                   | 73.8             | 91.7             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
-| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-cls.pt) | 224                   | 76.8             | 93.5             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
-| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-cls.pt) | 224                   | 76.8             | 93.5             | 163.0                          | 0.87                                | 37.5               | 99.7                     |
-| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-cls.pt) | 224                   | 79.0             | 94.6             | 232.0                          | 1.01                                | 57.4               | 154.8                    |
+| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224                   | 69.0             | 88.3             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
+| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224                   | 73.8             | 91.7             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
+| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224                   | 76.8             | 93.5             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
+| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224                   | 76.8             | 93.5             | 163.0                          | 0.87                                | 37.5               | 99.7                     |
+| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224                   | 79.0             | 94.6             | 232.0                          | 1.01                                | 57.4               | 154.8                    |
 
 ## Key Features
 
@@ -49,7 +49,7 @@ To train a deep learning model on the ImageNet dataset for 100 [epochs](https://
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenet", epochs=100, imgsz=224)
@@ -59,7 +59,7 @@ To train a deep learning model on the ImageNet dataset for 100 [epochs](https://
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenet model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 ## Sample Images and Annotations
@@ -110,7 +110,7 @@ To use a pretrained Ultralytics YOLO model for image classification on the Image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenet", epochs=100, imgsz=224)
@@ -120,14 +120,14 @@ To use a pretrained Ultralytics YOLO model for image classification on the Image
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenet model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 For more in-depth training instruction, refer to our [Training page](../../modes/train.md).
 
-### Why should I use the Ultralytics YOLOv8 pretrained models for my ImageNet dataset projects?
+### Why should I use the Ultralytics YOLO11 pretrained models for my ImageNet dataset projects?
 
-Ultralytics YOLOv8 pretrained models offer state-of-the-art performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for various computer vision tasks. For example, the YOLOv8n-cls model, with a top-1 accuracy of 69.0% and a top-5 accuracy of 88.3%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLOv8 models in the [ImageNet Pretrained Models section](#imagenet-pretrained-models).
+Ultralytics YOLO11 pretrained models offer state-of-the-art performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for various computer vision tasks. For example, the YOLO11n-cls model, with a top-1 accuracy of 69.0% and a top-5 accuracy of 88.3%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLO11 models in the [ImageNet Pretrained Models section](#imagenet-pretrained-models).
 
 ### How is the ImageNet dataset structured, and why is it important?
 

docs/en/datasets/classify/imagenet10.md

@@ -35,7 +35,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenet10", epochs=5, imgsz=224)
@@ -45,7 +45,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenet10 model=yolov8n-cls.pt epochs=5 imgsz=224
+        yolo classify train data=imagenet10 model=yolo11n-cls.pt epochs=5 imgsz=224
         ```
 
 ## Sample Images and Annotations
@@ -94,7 +94,7 @@ To test your deep learning model on the ImageNet10 dataset with an image size of
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenet10", epochs=5, imgsz=224)
@@ -104,7 +104,7 @@ To test your deep learning model on the ImageNet10 dataset with an image size of
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenet10 model=yolov8n-cls.pt epochs=5 imgsz=224
+        yolo classify train data=imagenet10 model=yolo11n-cls.pt epochs=5 imgsz=224
         ```
 
 Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.

docs/en/datasets/classify/imagenette.md

@@ -37,7 +37,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenette", epochs=100, imgsz=224)
@@ -47,7 +47,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 ## Sample Images and Annotations
@@ -72,7 +72,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model with ImageNette160
         results = model.train(data="imagenette160", epochs=100, imgsz=160)
@@ -82,7 +82,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
 
         ```bash
         # Start training from a pretrained *.pt model with ImageNette160
-        yolo classify train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160
+        yolo classify train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160
         ```
 
 !!! example "Train Example with ImageNette320"
@@ -93,7 +93,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model with ImageNette320
         results = model.train(data="imagenette320", epochs=100, imgsz=320)
@@ -103,7 +103,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
 
         ```bash
         # Start training from a pretrained *.pt model with ImageNette320
-        yolo classify train data=imagenette320 model=yolov8n-cls.pt epochs=100 imgsz=320
+        yolo classify train data=imagenette320 model=yolo11n-cls.pt epochs=100 imgsz=320
         ```
 
 These smaller versions of the dataset allow for rapid iterations during the development process while still providing valuable and realistic image classification tasks.
@@ -130,7 +130,7 @@ To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ult
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagenette", epochs=100, imgsz=224)
@@ -140,7 +140,7 @@ To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ult
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 For more details, see the [Training](../../modes/train.md) documentation page.
@@ -167,7 +167,7 @@ Yes, the ImageNette dataset is also available in two resized versions: ImageNett
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")
+        model = YOLO("yolo11n-cls.pt")
 
         # Train the model with ImageNette160
         results = model.train(data="imagenette160", epochs=100, imgsz=160)
@@ -177,7 +177,7 @@ Yes, the ImageNette dataset is also available in two resized versions: ImageNett
 
         ```bash
         # Start training from a pretrained *.pt model with ImageNette160
-        yolo detect train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160
+        yolo detect train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160
         ```
 
 For more information, refer to [Training with ImageNette160 and ImageNette320](#imagenette160-and-imagenette320).

docs/en/datasets/classify/imagewoof.md

@@ -34,7 +34,7 @@ To train a CNN model on the ImageWoof dataset for 100 [epochs](https://www.ultra
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="imagewoof", epochs=100, imgsz=224)
@@ -44,7 +44,7 @@ To train a CNN model on the ImageWoof dataset for 100 [epochs](https://www.ultra
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagewoof model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 ## Dataset Variants
@@ -67,7 +67,7 @@ To use these variants in your training, simply replace 'imagewoof' in the datase
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # For medium-sized dataset
         model.train(data="imagewoof320", epochs=100, imgsz=224)
@@ -80,7 +80,7 @@ To use these variants in your training, simply replace 'imagewoof' in the datase
 
         ```bash
         # Load a pretrained model and train on the small-sized dataset
-        yolo classify train model=yolov8n-cls.pt data=imagewoof320 epochs=100 imgsz=224
+        yolo classify train model=yolo11n-cls.pt data=imagewoof320 epochs=100 imgsz=224
         ```
 
 It's important to note that using smaller images will likely yield lower performance in terms of classification accuracy. However, it's an excellent way to iterate quickly in the early stages of model development and prototyping.
@@ -116,7 +116,7 @@ To train a [Convolutional Neural Network](https://www.ultralytics.com/glossary/c
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8n-cls.pt")  # Load a pretrained model
+        model = YOLO("yolo11n-cls.pt")  # Load a pretrained model
         results = model.train(data="imagewoof", epochs=100, imgsz=224)
         ```
 
@@ -124,7 +124,7 @@ To train a [Convolutional Neural Network](https://www.ultralytics.com/glossary/c
     === "CLI"
 
         ```bash
-        yolo classify train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagewoof model=yolo11n-cls.pt epochs=100 imgsz=224
         ```
 
 For more details on available training arguments, refer to the [Training](../../modes/train.md) page.

docs/en/datasets/classify/index.md

@@ -86,7 +86,7 @@ This structured approach ensures that the model can effectively learn from well-
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
@@ -96,7 +96,7 @@ This structured approach ensures that the model can effectively learn from well-
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640
+        yolo detect train data=path/to/data model=yolo11n-cls.pt epochs=100 imgsz=640
         ```
 
 ## Supported Datasets
@@ -170,7 +170,7 @@ To use your own dataset with Ultralytics YOLO, ensure it follows the specified d
 from ultralytics import YOLO
 
 # Load a model
-model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
 # Train the model
 results = model.train(data="path/to/your/dataset", epochs=100, imgsz=640)
@@ -182,7 +182,7 @@ More details can be found in the [Adding your own dataset](#adding-your-own-data
 
 Ultralytics YOLO offers several benefits for image classification, including:
 
-- **Pretrained Models**: Load pretrained models like `yolov8n-cls.pt` to jump-start your training process.
+- **Pretrained Models**: Load pretrained models like `yolo11n-cls.pt` to jump-start your training process.
 - **Ease of Use**: Simple API and CLI commands for training and evaluation.
 - **High Performance**: State-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed, ideal for real-time applications.
 - **Support for Multiple Datasets**: Seamless integration with various popular datasets like CIFAR-10, ImageNet, and more.
@@ -202,7 +202,7 @@ Training a model using Ultralytics YOLO can be done easily in both Python and CL
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model
 
         # Train the model
         results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
@@ -213,7 +213,7 @@ Training a model using Ultralytics YOLO can be done easily in both Python and CL
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640
+        yolo detect train data=path/to/data model=yolo11n-cls.pt epochs=100 imgsz=640
         ```
 
 These examples demonstrate the straightforward process of training a YOLO model using either approach. For more information, visit the [Usage](#usage) section.

docs/en/datasets/classify/mnist.md

@@ -42,7 +42,7 @@ To train a CNN model on the MNIST dataset for 100 [epochs](https://www.ultralyti
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="mnist", epochs=100, imgsz=32)
@@ -52,7 +52,7 @@ To train a CNN model on the MNIST dataset for 100 [epochs](https://www.ultralyti
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=mnist model=yolo11n-cls.pt epochs=100 imgsz=28
         ```
 
 ## Sample Images and Annotations
@@ -103,7 +103,7 @@ To train a model on the MNIST dataset using Ultralytics YOLO, you can follow the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="mnist", epochs=100, imgsz=32)
@@ -113,7 +113,7 @@ To train a model on the MNIST dataset using Ultralytics YOLO, you can follow the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo classify train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=mnist model=yolo11n-cls.pt epochs=100 imgsz=28
         ```
 
 For a detailed list of available training arguments, refer to the [Training](../../modes/train.md) page.

docs/en/datasets/detect/african-wildlife.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Explore our African Wildlife Dataset featuring images of buffalo, elephant, rhino, and zebra for training computer vision models. Ideal for research and conservation.
-keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLOv8, wildlife research, conservation, dataset
+keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLO11, wildlife research, conservation, dataset
 ---
 
 # African Wildlife Dataset
@@ -16,7 +16,7 @@ This dataset showcases four common animal classes typically found in South Afric
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> African Wildlife Animals Detection using Ultralytics YOLOv8
+  <strong>Watch:</strong> African Wildlife Animals Detection using Ultralytics YOLO11
 </p>
 
 ## Dataset Structure
@@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
 
 ## Usage
 
-To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
+To train a YOLO11n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -53,7 +53,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https:
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
@@ -63,7 +63,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https:
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=african-wildlife.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=african-wildlife.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 !!! example "Inference Example"
@@ -107,9 +107,9 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
 
 The African Wildlife Dataset includes images of four common animal species found in South African nature reserves: buffalo, elephant, rhino, and zebra. It is a valuable resource for training computer vision algorithms in object detection and animal identification. The dataset supports various tasks like object tracking, research, and conservation efforts. For more information on its structure and applications, refer to the [Dataset Structure](#dataset-structure) section and [Applications](#applications) of the dataset.
 
-### How do I train a YOLOv8 model using the African Wildlife Dataset?
+### How do I train a YOLO11 model using the African Wildlife Dataset?
 
-You can train a YOLOv8 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLOv8n model for 100 epochs with an image size of 640:
+You can train a YOLO11 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLO11n model for 100 epochs with an image size of 640:
 
 !!! example
 
@@ -119,7 +119,7 @@ You can train a YOLOv8 model on the African Wildlife Dataset by using the `afric
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
@@ -129,7 +129,7 @@ You can train a YOLOv8 model on the African Wildlife Dataset by using the `afric
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=african-wildlife.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=african-wildlife.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For additional training parameters and options, refer to the [Training](../../modes/train.md) documentation.

docs/en/datasets/detect/argoverse.md

@@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the Argoverse dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the Argoverse dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -53,7 +53,7 @@ To train a YOLOv8n model on the Argoverse dataset for 100 [epochs](https://www.u
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
@@ -63,7 +63,7 @@ To train a YOLOv8n model on the Argoverse dataset for 100 [epochs](https://www.u
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=Argoverse.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=Argoverse.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -104,7 +104,7 @@ The [Argoverse](https://www.argoverse.org/) dataset, developed by Argo AI, suppo
 
 ### How can I train an Ultralytics YOLO model using the Argoverse dataset?
 
-To train a YOLOv8 model with the Argoverse dataset, use the provided YAML configuration file and the following code:
+To train a YOLO11 model with the Argoverse dataset, use the provided YAML configuration file and the following code:
 
 !!! example "Train Example"
 
@@ -114,7 +114,7 @@ To train a YOLOv8 model with the Argoverse dataset, use the provided YAML config
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
@@ -125,7 +125,7 @@ To train a YOLOv8 model with the Argoverse dataset, use the provided YAML config
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=Argoverse.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=Argoverse.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For a detailed explanation of the arguments, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/detect/brain-tumor.md

@@ -42,7 +42,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the brain tumor dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, utilize the provided code snippets. For a detailed list of available arguments, consult the model's [Training](../../modes/train.md) page.
+To train a YOLO11n model on the brain tumor dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, utilize the provided code snippets. For a detailed list of available arguments, consult the model's [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -52,7 +52,7 @@ To train a YOLOv8n model on the brain tumor dataset for 100 [epochs](https://www
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
@@ -62,7 +62,7 @@ To train a YOLOv8n model on the brain tumor dataset for 100 [epochs](https://www
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=brain-tumor.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=brain-tumor.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 !!! example "Inference Example"
@@ -106,9 +106,9 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
 
 The brain tumor dataset is divided into two subsets: the **training set** consists of 893 images with corresponding annotations, while the **testing set** comprises 223 images with paired annotations. This structured division aids in developing robust and accurate computer vision models for detecting brain tumors. For more information on the dataset structure, visit the [Dataset Structure](#dataset-structure) section.
 
-### How can I train a YOLOv8 model on the brain tumor dataset using Ultralytics?
+### How can I train a YOLO11 model on the brain tumor dataset using Ultralytics?
 
-You can train a YOLOv8 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:
+You can train a YOLO11 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:
 
 !!! example "Train Example"
 
@@ -118,7 +118,7 @@ You can train a YOLOv8 model on the brain tumor dataset for 100 epochs with an i
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
@@ -129,7 +129,7 @@ You can train a YOLOv8 model on the brain tumor dataset for 100 epochs with an i
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=brain-tumor.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=brain-tumor.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For a detailed list of available arguments, refer to the [Training](../../modes/train.md) page.
@@ -138,9 +138,9 @@ For a detailed list of available arguments, refer to the [Training](../../modes/
 
 Using the brain tumor dataset in AI projects enables early diagnosis and treatment planning for brain tumors. It helps in automating brain tumor identification through computer vision, facilitating accurate and timely medical interventions, and supporting personalized treatment strategies. This application holds significant potential in improving patient outcomes and medical efficiencies.
 
-### How do I perform inference using a fine-tuned YOLOv8 model on the brain tumor dataset?
+### How do I perform inference using a fine-tuned YOLO11 model on the brain tumor dataset?
 
-Inference using a fine-tuned YOLOv8 model can be performed with either Python or CLI approaches. Here are the examples:
+Inference using a fine-tuned YOLO11 model can be performed with either Python or CLI approaches. Here are the examples:
 
 !!! example "Inference Example"
 

docs/en/datasets/detect/coco.md

@@ -21,13 +21,13 @@ The [COCO](https://cocodataset.org/#home) (Common Objects in Context) dataset is
 
 ## COCO Pretrained Models
 
-| Model                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| Model                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
 | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
-| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |
-| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |
-| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |
-| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640                   | 52.9                 | 375.2                          | 2.39                                | 43.7               | 165.2             |
-| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |
+| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640                   | 39.5                 | 56.1 ± 0.8                     | 1.5 ± 0.0                           | 2.6                | 6.5               |
+| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640                   | 47.0                 | 90.0 ± 1.2                     | 2.5 ± 0.0                           | 9.4                | 21.5              |
+| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640                   | 51.5                 | 183.2 ± 2.0                    | 4.7 ± 0.1                           | 20.1               | 68.0              |
+| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640                   | 53.4                 | 238.6 ± 1.4                    | 6.2 ± 0.1                           | 25.3               | 86.9              |
+| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640                   | 54.7                 | 462.8 ± 6.7                    | 11.3 ± 0.2                          | 56.9               | 194.9             |
 
 ## Key Features
 
@@ -60,7 +60,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the COCO dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the COCO dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -70,7 +70,7 @@ To train a YOLOv8n model on the COCO dataset for 100 [epochs](https://www.ultral
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco.yaml", epochs=100, imgsz=640)
@@ -80,7 +80,7 @@ To train a YOLOv8n model on the COCO dataset for 100 [epochs](https://www.ultral
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=coco.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -122,7 +122,7 @@ The [COCO dataset](https://cocodataset.org/#home) (Common Objects in Context) is
 
 ### How can I train a YOLO model using the COCO dataset?
 
-To train a YOLOv8 model using the COCO dataset, you can use the following code snippets:
+To train a YOLO11 model using the COCO dataset, you can use the following code snippets:
 
 !!! example "Train Example"
 
@@ -132,7 +132,7 @@ To train a YOLOv8 model using the COCO dataset, you can use the following code s
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco.yaml", epochs=100, imgsz=640)
@@ -142,7 +142,7 @@ To train a YOLOv8 model using the COCO dataset, you can use the following code s
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=coco.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 Refer to the [Training page](../../modes/train.md) for more details on available arguments.
@@ -156,13 +156,13 @@ The COCO dataset includes:
 - Standardized evaluation metrics for object detection (mAP) and segmentation (mean Average Recall, mAR).
 - **Mosaicing** technique in training batches to enhance model generalization across various object sizes and contexts.
 
-### Where can I find pretrained YOLOv8 models trained on the COCO dataset?
+### Where can I find pretrained YOLO11 models trained on the COCO dataset?
 
-Pretrained YOLOv8 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
+Pretrained YOLO11 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
 
-- [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt)
-- [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt)
-- [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt)
+- [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt)
+- [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt)
+- [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt)
 
 These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.
 

docs/en/datasets/detect/coco8.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Explore the Ultralytics COCO8 dataset, a versatile and manageable set of 8 images perfect for testing object detection models and training pipelines.
-keywords: COCO8, Ultralytics, dataset, object detection, YOLOv8, training, validation, machine learning, computer vision
+keywords: COCO8, Ultralytics, dataset, object detection, YOLO11, training, validation, machine learning, computer vision
 ---
 
 # COCO8 Dataset
@@ -21,7 +21,7 @@ keywords: COCO8, Ultralytics, dataset, object detection, YOLOv8, training, valid
   <strong>Watch:</strong> Ultralytics COCO Dataset Overview
 </p>
 
-This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
 
 ## Dataset YAML
 
@@ -35,7 +35,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -45,7 +45,7 @@ To train a YOLOv8n model on the COCO8 dataset for 100 [epochs](https://www.ultra
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
@@ -55,7 +55,7 @@ To train a YOLOv8n model on the COCO8 dataset for 100 [epochs](https://www.ultra
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -95,9 +95,9 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
 
 The Ultralytics COCO8 dataset is a compact yet versatile object detection dataset consisting of the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It is designed for testing and debugging object detection models and experimentation with new detection approaches. Despite its small size, COCO8 offers enough diversity to act as a sanity check for your training pipelines before deploying larger datasets. For more details, view the [COCO8 dataset](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml).
 
-### How do I train a YOLOv8 model using the COCO8 dataset?
+### How do I train a YOLO11 model using the COCO8 dataset?
 
-To train a YOLOv8 model using the COCO8 dataset, you can employ either Python or CLI commands. Here's how you can start:
+To train a YOLO11 model using the COCO8 dataset, you can employ either Python or CLI commands. Here's how you can start:
 
 !!! example "Train Example"
 
@@ -107,7 +107,7 @@ To train a YOLOv8 model using the COCO8 dataset, you can employ either Python or
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
@@ -117,19 +117,19 @@ To train a YOLOv8 model using the COCO8 dataset, you can employ either Python or
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 ### Why should I use Ultralytics HUB for managing my COCO8 training?
 
-Ultralytics HUB is an all-in-one web tool designed to simplify the training and deployment of YOLO models, including the Ultralytics YOLOv8 models on the COCO8 dataset. It offers cloud training, real-time tracking, and seamless dataset management. HUB allows you to start training with a single click and avoids the complexities of manual setups. Discover more about [Ultralytics HUB](https://hub.ultralytics.com/) and its benefits.
+Ultralytics HUB is an all-in-one web tool designed to simplify the training and deployment of YOLO models, including the Ultralytics YOLO11 models on the COCO8 dataset. It offers cloud training, real-time tracking, and seamless dataset management. HUB allows you to start training with a single click and avoids the complexities of manual setups. Discover more about [Ultralytics HUB](https://hub.ultralytics.com/) and its benefits.
 
 ### What are the benefits of using mosaic augmentation in training with the COCO8 dataset?
 
 Mosaic augmentation, demonstrated in the COCO8 dataset, combines multiple images into a single image during training. This technique increases the variety of objects and scenes in each training batch, improving the model's ability to generalize across different object sizes, aspect ratios, and contexts. This results in a more robust object detection model. For more details, refer to the [training guide](#usage).
 
-### How can I validate my YOLOv8 model trained on the COCO8 dataset?
+### How can I validate my YOLO11 model trained on the COCO8 dataset?
 
-Validation of your YOLOv8 model trained on the COCO8 dataset can be performed using the model's validation commands. You can invoke the validation mode via CLI or Python script to evaluate the model's performance using precise metrics. For detailed instructions, visit the [Validation](../../modes/val.md) page.
+Validation of your YOLO11 model trained on the COCO8 dataset can be performed using the model's validation commands. You can invoke the validation mode via CLI or Python script to evaluate the model's performance using precise metrics. For detailed instructions, visit the [Validation](../../modes/val.md) page.

docs/en/datasets/detect/globalwheat2020.md

@@ -38,7 +38,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -48,7 +48,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
@@ -58,7 +58,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=GlobalWheat2020.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=GlobalWheat2020.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -96,9 +96,9 @@ We would like to acknowledge the researchers and institutions that contributed t
 
 The Global Wheat Head Dataset is primarily used for developing and training deep learning models aimed at wheat head detection. This is crucial for applications in wheat phenotyping and crop management, allowing for more accurate estimations of wheat head density, size, and overall crop yield potential. Accurate detection methods help in assessing crop health and maturity, essential for efficient crop management.
 
-### How do I train a YOLOv8n model on the Global Wheat Head Dataset?
+### How do I train a YOLO11n model on the Global Wheat Head Dataset?
 
-To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
+To train a YOLO11n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
 
 !!! example "Train Example"
 
@@ -108,7 +108,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the follo
         from ultralytics import YOLO
 
         # Load a pre-trained model (recommended for training)
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Train the model
         results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
@@ -118,7 +118,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the follo
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=GlobalWheat2020.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=GlobalWheat2020.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/detect/index.md

@@ -56,7 +56,7 @@ Here's how you can use these formats to train your model:
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
@@ -66,7 +66,7 @@ Here's how you can use these formats to train your model:
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Supported Datasets
@@ -158,11 +158,11 @@ Ultralytics YOLO supports a wide range of datasets, including:
 - [Objects365](objects365.md)
 - [OpenImagesV7](open-images-v7.md)
 
-Each dataset page provides detailed information on the structure and usage tailored for efficient YOLOv8 training. Explore the full list in the [Supported Datasets](#supported-datasets) section.
+Each dataset page provides detailed information on the structure and usage tailored for efficient YOLO11 training. Explore the full list in the [Supported Datasets](#supported-datasets) section.
 
-### How do I start training a YOLOv8 model using my dataset?
+### How do I start training a YOLO11 model using my dataset?
 
-To start training a YOLOv8 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:
+To start training a YOLO11 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:
 
 !!! example
 
@@ -171,18 +171,18 @@ To start training a YOLOv8 model, ensure your dataset is formatted correctly and
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8n.pt")  # Load a pretrained model
+        model = YOLO("yolo11n.pt")  # Load a pretrained model
         results = model.train(data="path/to/your_dataset.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
 
         ```bash
-        yolo detect train data=path/to/your_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=path/to/your_dataset.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 Refer to the [Usage](#usage) section for more details on utilizing different modes, including CLI commands.
 
 ### Where can I find practical examples of using Ultralytics YOLO for object detection?
 
-Ultralytics provides numerous examples and practical guides for using YOLOv8 in diverse applications. For a comprehensive overview, visit the [Ultralytics Blog](https://www.ultralytics.com/blog) where you can find case studies, detailed tutorials, and community stories showcasing object detection, segmentation, and more with YOLOv8. For specific examples, check the [Usage](../../modes/predict.md) section in the documentation.
+Ultralytics provides numerous examples and practical guides for using YOLO11 in diverse applications. For a comprehensive overview, visit the [Ultralytics Blog](https://www.ultralytics.com/blog) where you can find case studies, detailed tutorials, and community stories showcasing object detection, segmentation, and more with YOLO11. For specific examples, check the [Usage](../../modes/predict.md) section in the documentation.

docs/en/datasets/detect/lvis.md

@@ -56,7 +56,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the LVIS dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the LVIS dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -66,7 +66,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 [epochs](https://www.ultral
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
@@ -76,7 +76,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 [epochs](https://www.ultral
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=lvis.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=lvis.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -114,9 +114,9 @@ We would like to acknowledge the LVIS Consortium for creating and maintaining th
 
 The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale dataset with fine-grained vocabulary-level annotations developed by Facebook AI Research (FAIR). It is primarily used for object detection and instance segmentation, featuring over 1203 object categories and 2 million instance annotations. Researchers and practitioners use it to train and benchmark models like Ultralytics YOLO for advanced computer vision tasks. The dataset's extensive size and diversity make it an essential resource for pushing the boundaries of model performance in detection and segmentation.
 
-### How can I train a YOLOv8n model using the LVIS dataset?
+### How can I train a YOLO11n model using the LVIS dataset?
 
-To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size of 640, follow the example below. This process utilizes Ultralytics' framework, which offers comprehensive training features.
+To train a YOLO11n model on the LVIS dataset for 100 epochs with an image size of 640, follow the example below. This process utilizes Ultralytics' framework, which offers comprehensive training features.
 
 !!! example "Train Example"
 
@@ -126,7 +126,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size o
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
@@ -137,7 +137,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size o
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=lvis.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=lvis.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For detailed training configurations, refer to the [Training](../../modes/train.md) documentation.
@@ -148,7 +148,7 @@ The images in the LVIS dataset are the same as those in the [COCO dataset](./coc
 
 ### Why should I use Ultralytics YOLO for training on the LVIS dataset?
 
-Ultralytics YOLO models, including the latest YOLOv8, are optimized for real-time object detection with state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed. They support a wide range of annotations, such as the fine-grained ones provided by the LVIS dataset, making them ideal for advanced computer vision applications. Moreover, Ultralytics offers seamless integration with various [training](../../modes/train.md), [validation](../../modes/val.md), and [prediction](../../modes/predict.md) modes, ensuring efficient model development and deployment.
+Ultralytics YOLO models, including the latest YOLO11, are optimized for real-time object detection with state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed. They support a wide range of annotations, such as the fine-grained ones provided by the LVIS dataset, making them ideal for advanced computer vision applications. Moreover, Ultralytics offers seamless integration with various [training](../../modes/train.md), [validation](../../modes/val.md), and [prediction](../../modes/predict.md) modes, ensuring efficient model development and deployment.
 
 ### Can I see some sample annotations from the LVIS dataset?
 

docs/en/datasets/detect/objects365.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Explore the Objects365 Dataset with 2M images and 30M bounding boxes across 365 categories. Enhance your object detection models with diverse, high-quality data.
-keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLOv8, high-resolution images, dataset configuration
+keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLO11, high-resolution images, dataset configuration
 ---
 
 # Objects365 Dataset
@@ -38,7 +38,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the Objects365 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the Objects365 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -48,7 +48,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 [epochs](https://www.
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
@@ -58,7 +58,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 [epochs](https://www.
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=Objects365.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -97,9 +97,9 @@ We would like to acknowledge the team of researchers who created and maintain th
 
 The [Objects365 dataset](https://www.objects365.org/) is designed for object detection tasks in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision. It provides a large-scale, high-quality dataset with 2 million annotated images and 30 million bounding boxes across 365 categories. Leveraging such a diverse dataset helps improve the performance and generalization of object detection models, making it invaluable for research and development in the field.
 
-### How can I train a YOLOv8 model on the Objects365 dataset?
+### How can I train a YOLO11 model on the Objects365 dataset?
 
-To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
+To train a YOLO11n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
 
 !!! example "Train Example"
 
@@ -109,7 +109,7 @@ To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an ima
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
@@ -119,7 +119,7 @@ To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an ima
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=Objects365.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.

docs/en/datasets/detect/open-images-v7.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the comprehensive Open Images V7 dataset by Google. Learn about its annotations, applications, and use YOLOv8 pretrained models for computer vision tasks.
-keywords: Open Images V7, Google dataset, computer vision, YOLOv8 models, object detection, image segmentation, visual relationships, AI research, Ultralytics
+description: Explore the comprehensive Open Images V7 dataset by Google. Learn about its annotations, applications, and use YOLO11 pretrained models for computer vision tasks.
+keywords: Open Images V7, Google dataset, computer vision, YOLO11 models, object detection, image segmentation, visual relationships, AI research, Ultralytics
 ---
 
 # Open Images V7 Dataset
@@ -69,7 +69,7 @@ Typically, datasets come with a YAML (Yet Another Markup Language) file that del
 
 ## Usage
 
-To train a YOLOv8n model on the Open Images V7 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the Open Images V7 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! warning
 
@@ -87,8 +87,8 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 [epochs](https://
         ```python
         from ultralytics import YOLO
 
-        # Load a COCO-pretrained YOLOv8n model
-        model = YOLO("yolov8n.pt")
+        # Load a COCO-pretrained YOLO11n model
+        model = YOLO("yolo11n.pt")
 
         # Train the model on the Open Images V7 dataset
         results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
@@ -97,8 +97,8 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 [epochs](https://
     === "CLI"
 
         ```bash
-        # Train a COCO-pretrained YOLOv8n model on the Open Images V7 dataset
-        yolo detect train data=open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640
+        # Train a COCO-pretrained YOLO11n model on the Open Images V7 dataset
+        yolo detect train data=open-images-v7.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -136,9 +136,9 @@ A heartfelt acknowledgment goes out to the Google AI team for creating and maint
 
 Open Images V7 is an extensive and versatile dataset created by Google, designed to advance research in computer vision. It includes image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives, making it ideal for various computer vision tasks such as object detection, segmentation, and relationship detection.
 
-### How do I train a YOLOv8 model on the Open Images V7 dataset?
+### How do I train a YOLO11 model on the Open Images V7 dataset?
 
-To train a YOLOv8 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLOv8n model for 100 epochs with an image size of 640:
+To train a YOLO11 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLO11n model for 100 epochs with an image size of 640:
 
 !!! example "Train Example"
 
@@ -147,8 +147,8 @@ To train a YOLOv8 model on the Open Images V7 dataset, you can use both Python a
         ```python
         from ultralytics import YOLO
 
-        # Load a COCO-pretrained YOLOv8n model
-        model = YOLO("yolov8n.pt")
+        # Load a COCO-pretrained YOLO11n model
+        model = YOLO("yolo11n.pt")
 
         # Train the model on the Open Images V7 dataset
         results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
@@ -158,8 +158,8 @@ To train a YOLOv8 model on the Open Images V7 dataset, you can use both Python a
     === "CLI"
 
         ```bash
-        # Train a COCO-pretrained YOLOv8n model on the Open Images V7 dataset
-        yolo detect train data=open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640
+        # Train a COCO-pretrained YOLO11n model on the Open Images V7 dataset
+        yolo detect train data=open-images-v7.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For more details on arguments and settings, refer to the [Training](../../modes/train.md) page.

docs/en/datasets/detect/roboflow-100.md

@@ -67,7 +67,7 @@ Dataset benchmarking evaluates machine learning model performance on specific da
             if path.exists():
                 # Fix YAML file and run training
                 benchmark.fix_yaml(str(path))
-                os.system(f"yolo detect train data={path} model=yolov8s.pt epochs=1 batch=16")
+                os.system(f"yolo detect train data={path} model=yolo11s.pt epochs=1 batch=16")
 
                 # Run validation and evaluate
                 os.system(f"yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1")
@@ -165,7 +165,7 @@ To use the Roboflow 100 dataset for benchmarking, you can implement the RF100Ben
             if path.exists():
                 # Fix YAML file and run training
                 benchmark.fix_yaml(str(path))
-                os.system(f"yolo detect train data={path} model=yolov8s.pt epochs=1 batch=16")
+                os.system(f"yolo detect train data={path} model=yolo11n.pt epochs=1 batch=16")
 
                 # Run validation and evaluate
                 os.system(f"yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1")

docs/en/datasets/detect/signature.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Discover the Signature Detection Dataset for training models to identify and verify human signatures in various documents. Perfect for document verification and fraud prevention.
-keywords: Signature Detection Dataset, document verification, fraud detection, computer vision, YOLOv8, Ultralytics, annotated signatures, training dataset
+keywords: Signature Detection Dataset, document verification, fraud detection, computer vision, YOLO11, Ultralytics, annotated signatures, training dataset
 ---
 
 # Signature Detection Dataset
@@ -31,7 +31,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
 
 ## Usage
 
-To train a YOLOv8n model on the signature detection dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
+To train a YOLO11n model on the signature detection dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -41,7 +41,7 @@ To train a YOLOv8n model on the signature detection dataset for 100 [epochs](htt
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="signature.yaml", epochs=100, imgsz=640)
@@ -51,7 +51,7 @@ To train a YOLOv8n model on the signature detection dataset for 100 [epochs](htt
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=signature.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=signature.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 !!! example "Inference Example"
@@ -95,9 +95,9 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
 
 The Signature Detection Dataset is a collection of annotated images aimed at detecting human signatures within various document types. It can be applied in computer vision tasks such as [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking, primarily for document verification, fraud detection, and archival research. This dataset helps train models to recognize signatures in different contexts, making it valuable for both research and practical applications.
 
-### How do I train a YOLOv8n model on the Signature Detection Dataset?
+### How do I train a YOLO11n model on the Signature Detection Dataset?
 
-To train a YOLOv8n model on the Signature Detection Dataset, follow these steps:
+To train a YOLO11n model on the Signature Detection Dataset, follow these steps:
 
 1. Download the `signature.yaml` dataset configuration file from [signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
 2. Use the following Python script or CLI command to start training:
@@ -110,7 +110,7 @@ To train a YOLOv8n model on the Signature Detection Dataset, follow these steps:
         from ultralytics import YOLO
 
         # Load a pretrained model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Train the model
         results = model.train(data="signature.yaml", epochs=100, imgsz=640)
@@ -119,7 +119,7 @@ To train a YOLOv8n model on the Signature Detection Dataset, follow these steps:
     === "CLI"
 
         ```bash
-        yolo detect train data=signature.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=signature.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For more details, refer to the [Training](../../modes/train.md) page.

docs/en/datasets/detect/sku-110k.md

@@ -51,7 +51,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the SKU-110K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the SKU-110K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -61,7 +61,7 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 [epochs](https://www.ul
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
@@ -71,7 +71,7 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 [epochs](https://www.ul
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=SKU-110K.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=SKU-110K.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -109,9 +109,9 @@ We would like to acknowledge Eran Goldman et al. for creating and maintaining th
 
 The SKU-110k dataset consists of densely packed retail shelf images designed to aid research in object detection tasks. Developed by Eran Goldman et al., it includes over 110,000 unique SKU categories. Its importance lies in its ability to challenge state-of-the-art object detectors with diverse object appearances and close proximity, making it an invaluable resource for researchers and practitioners in computer vision. Learn more about the dataset's structure and applications in our [SKU-110k Dataset](#sku-110k-dataset) section.
 
-### How do I train a YOLOv8 model using the SKU-110k dataset?
+### How do I train a YOLO11 model using the SKU-110k dataset?
 
-Training a YOLOv8 model on the SKU-110k dataset is straightforward. Here's an example to train a YOLOv8n model for 100 epochs with an image size of 640:
+Training a YOLO11 model on the SKU-110k dataset is straightforward. Here's an example to train a YOLO11n model for 100 epochs with an image size of 640:
 
 !!! example "Train Example"
 
@@ -121,7 +121,7 @@ Training a YOLOv8 model on the SKU-110k dataset is straightforward. Here's an ex
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
@@ -132,7 +132,7 @@ Training a YOLOv8 model on the SKU-110k dataset is straightforward. Here's an ex
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=SKU-110K.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=SKU-110K.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/detect/visdrone.md

@@ -47,7 +47,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the VisDrone dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the VisDrone dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -57,7 +57,7 @@ To train a YOLOv8n model on the VisDrone dataset for 100 [epochs](https://www.ul
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
@@ -67,7 +67,7 @@ To train a YOLOv8n model on the VisDrone dataset for 100 [epochs](https://www.ul
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=VisDrone.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=VisDrone.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -113,9 +113,9 @@ The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-
 - **Diversity**: Collected across 14 cities, in urban and rural settings, under different weather and lighting conditions.
 - **Tasks**: Split into five main tasks—object detection in images and videos, single-object and multi-object tracking, and crowd counting.
 
-### How can I use the VisDrone Dataset to train a YOLOv8 model with Ultralytics?
+### How can I use the VisDrone Dataset to train a YOLO11 model with Ultralytics?
 
-To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
+To train a YOLO11 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
 
 !!! example "Train Example"
 
@@ -125,7 +125,7 @@ To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image siz
         from ultralytics import YOLO
 
         # Load a pretrained model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Train the model
         results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
@@ -135,7 +135,7 @@ To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image siz
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=VisDrone.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=VisDrone.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For additional configuration options, please refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/detect/voc.md

@@ -39,7 +39,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n model on the VOC dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n model on the VOC dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -49,7 +49,7 @@ To train a YOLOv8n model on the VOC dataset for 100 [epochs](https://www.ultraly
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
@@ -59,7 +59,7 @@ To train a YOLOv8n model on the VOC dataset for 100 [epochs](https://www.ultraly
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=VOC.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=VOC.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -99,9 +99,9 @@ We would like to acknowledge the PASCAL VOC Consortium for creating and maintain
 
 The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes) dataset is a renowned benchmark for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in computer vision. It includes comprehensive annotations like bounding boxes, class labels, and segmentation masks across 20 different object categories. Researchers use it widely to evaluate the performance of models like Faster R-CNN, YOLO, and Mask R-CNN due to its standardized evaluation metrics such as mean Average Precision (mAP).
 
-### How do I train a YOLOv8 model using the VOC dataset?
+### How do I train a YOLO11 model using the VOC dataset?
 
-To train a YOLOv8 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLOv8n model for 100 epochs with an image size of 640:
+To train a YOLO11 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLO11n model for 100 epochs with an image size of 640:
 
 !!! example "Train Example"
 
@@ -111,7 +111,7 @@ To train a YOLOv8 model with the VOC dataset, you need the dataset configuration
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
@@ -121,7 +121,7 @@ To train a YOLOv8 model with the VOC dataset, you need the dataset configuration
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=VOC.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=VOC.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ### What are the primary challenges included in the VOC dataset?

docs/en/datasets/detect/xview.md

@@ -52,7 +52,7 @@ To train a model on the xView dataset for 100 [epochs](https://www.ultralytics.c
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="xView.yaml", epochs=100, imgsz=640)
@@ -62,7 +62,7 @@ To train a model on the xView dataset for 100 [epochs](https://www.ultralytics.c
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=xView.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=xView.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -114,7 +114,7 @@ To train a model on the xView dataset using Ultralytics YOLO, follow these steps
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="xView.yaml", epochs=100, imgsz=640)
@@ -125,7 +125,7 @@ To train a model on the xView dataset using Ultralytics YOLO, follow these steps
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=xView.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=xView.yaml model=yolo11n.pt epochs=100 imgsz=640
         ```
 
 For detailed arguments and settings, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/explorer/api.md

@@ -36,7 +36,7 @@ pip install ultralytics[explorer]
 from ultralytics import Explorer
 
 # Create an Explorer object
-explorer = Explorer(data="coco128.yaml", model="yolov8n.pt")
+explorer = Explorer(data="coco128.yaml", model="yolo11n.pt")
 
 # Create embeddings for your dataset
 explorer.create_embeddings_table()
@@ -75,7 +75,7 @@ You get a pandas dataframe with the `limit` number of most similar data points t
         from ultralytics import Explorer
 
         # create an Explorer object
-        exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
+        exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
         exp.create_embeddings_table()
 
         similar = exp.get_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
@@ -95,7 +95,7 @@ You get a pandas dataframe with the `limit` number of most similar data points t
         from ultralytics import Explorer
 
         # create an Explorer object
-        exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
+        exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
         exp.create_embeddings_table()
 
         similar = exp.get_similar(idx=1, limit=10)
@@ -118,7 +118,7 @@ You can also plot the similar images using the `plot_similar` method. This metho
         from ultralytics import Explorer
 
         # create an Explorer object
-        exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
+        exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
         exp.create_embeddings_table()
 
         plt = exp.plot_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
@@ -131,7 +131,7 @@ You can also plot the similar images using the `plot_similar` method. This metho
         from ultralytics import Explorer
 
         # create an Explorer object
-        exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
+        exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
         exp.create_embeddings_table()
 
         plt = exp.plot_similar(idx=1, limit=10)
@@ -150,7 +150,7 @@ Note: This works using LLMs under the hood so the results are probabilistic and
     from ultralytics.data.explorer import plot_query_result
 
     # create an Explorer object
-    exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
+    exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
     exp.create_embeddings_table()
 
     df = exp.ask_ai("show me 100 images with exactly one person and 2 dogs. There can be other objects too")
@@ -171,7 +171,7 @@ You can run SQL queries on your dataset using the `sql_query` method. This metho
     from ultralytics import Explorer
 
     # create an Explorer object
-    exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
+    exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
     exp.create_embeddings_table()
 
     df = exp.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%'")
@@ -188,7 +188,7 @@ You can also plot the results of a SQL query using the `plot_sql_query` method.
     from ultralytics import Explorer
 
     # create an Explorer object
-    exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
+    exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
     exp.create_embeddings_table()
 
     # plot the SQL Query
@@ -235,7 +235,7 @@ Here are some examples of what you can do with the table:
     ```python
     from ultralytics import Explorer
 
-    exp = Explorer(model="yolov8n.pt")
+    exp = Explorer(model="yolo11n.pt")
     exp.create_embeddings_table()
     table = exp.table
 
@@ -359,7 +359,7 @@ You can use the Ultralytics Explorer API to perform similarity searches by creat
 from ultralytics import Explorer
 
 # Create an Explorer object
-explorer = Explorer(data="coco128.yaml", model="yolov8n.pt")
+explorer = Explorer(data="coco128.yaml", model="yolo11n.pt")
 explorer.create_embeddings_table()
 
 # Search for similar images to a given image
@@ -381,7 +381,7 @@ The Ask AI feature allows users to filter datasets using natural language querie
 from ultralytics import Explorer
 
 # Create an Explorer object
-explorer = Explorer(data="coco128.yaml", model="yolov8n.pt")
+explorer = Explorer(data="coco128.yaml", model="yolo11n.pt")
 explorer.create_embeddings_table()
 
 # Query with natural language

docs/en/datasets/explorer/explorer.ipynb

@@ -88,7 +88,7 @@
    },
    "outputs": [],
    "source": [
-    "exp = Explorer(\"VOC.yaml\", model=\"yolov8n.pt\")\n",
+    "exp = Explorer(\"VOC.yaml\", model=\"yolo11n.pt\")\n",
     "exp.create_embeddings_table()"
    ]
   },

docs/en/datasets/obb/dota-v2.md

@@ -108,8 +108,8 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
         ```python
         from ultralytics import YOLO
 
-        # Create a new YOLOv8n-OBB model from scratch
-        model = YOLO("yolov8n-obb.yaml")
+        # Create a new YOLO11n-OBB model from scratch
+        model = YOLO("yolo11n-obb.yaml")
 
         # Train the model on the DOTAv1 dataset
         results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
@@ -118,8 +118,8 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
     === "CLI"
 
         ```bash
-        # Train a new YOLOv8n-OBB model on the DOTAv1 dataset
-        yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=1024
+        # Train a new YOLO11n-OBB model on the DOTAv1 dataset
+        yolo obb train data=DOTAv1.yaml model=yolo11n-obb.pt epochs=100 imgsz=1024
         ```
 
 ## Sample Data and Annotations
@@ -176,8 +176,8 @@ To train a model on the DOTA dataset, you can use the following example with Ult
         ```python
         from ultralytics import YOLO
 
-        # Create a new YOLOv8n-OBB model from scratch
-        model = YOLO("yolov8n-obb.yaml")
+        # Create a new YOLO11n-OBB model from scratch
+        model = YOLO("yolo11n-obb.yaml")
 
         # Train the model on the DOTAv1 dataset
         results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
@@ -186,8 +186,8 @@ To train a model on the DOTA dataset, you can use the following example with Ult
     === "CLI"
 
         ```bash
-        # Train a new YOLOv8n-OBB model on the DOTAv1 dataset
-        yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=1024
+        # Train a new YOLO11n-OBB model on the DOTAv1 dataset
+        yolo obb train data=DOTAv1.yaml model=yolo11n-obb.pt epochs=100 imgsz=1024
         ```
 
 For more details on how to split and preprocess the DOTA images, refer to the [split DOTA images section](#split-dota-images).

docs/en/datasets/obb/dota8.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLOv8.
-keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, training models, oriented object detection, dataset YAML
+description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLO11.
+keywords: DOTA8 dataset, Ultralytics, YOLO11, object detection, debugging, training models, oriented object detection, dataset YAML
 ---
 
 # DOTA8 Dataset
@@ -10,7 +10,7 @@ keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, train
 
 [Ultralytics](https://www.ultralytics.com/) DOTA8 is a small, but versatile oriented [object detection](https://www.ultralytics.com/glossary/object-detection) dataset composed of the first 8 images of 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
-This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
 
 ## Dataset YAML
 
@@ -24,7 +24,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-obb model on the DOTA8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-obb model on the DOTA8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -34,7 +34,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 [epochs](https://www.u
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-obb.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-obb.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
@@ -44,7 +44,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 [epochs](https://www.u
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
+        yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -84,11 +84,11 @@ A special note of gratitude to the team behind the DOTA datasets for their comme
 
 ### What is the DOTA8 dataset and how can it be used?
 
-The DOTA8 dataset is a small, versatile oriented object detection dataset made up of the first 8 images from the DOTAv1 split set, with 4 images designated for training and 4 for validation. It's ideal for testing and debugging object detection models like Ultralytics YOLOv8. Due to its manageable size and diversity, it helps in identifying pipeline errors and running sanity checks before deploying larger datasets. Learn more about object detection with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics).
+The DOTA8 dataset is a small, versatile oriented object detection dataset made up of the first 8 images from the DOTAv1 split set, with 4 images designated for training and 4 for validation. It's ideal for testing and debugging object detection models like Ultralytics YOLO11. Due to its manageable size and diversity, it helps in identifying pipeline errors and running sanity checks before deploying larger datasets. Learn more about object detection with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics).
 
-### How do I train a YOLOv8 model using the DOTA8 dataset?
+### How do I train a YOLO11 model using the DOTA8 dataset?
 
-To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For comprehensive argument options, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For comprehensive argument options, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -98,7 +98,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-obb.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-obb.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
@@ -108,7 +108,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
+        yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
         ```
 
 ### What are the key features of the DOTA dataset and where can I access the YAML file?
@@ -119,6 +119,6 @@ The DOTA dataset is known for its large-scale benchmark and the challenges it pr
 
 Mosaicing combines multiple images into one during training, increasing the variety of objects and contexts within each batch. This improves a model's ability to generalize to different object sizes, aspect ratios, and scenes. This technique can be visually demonstrated through a training batch composed of mosaiced DOTA8 dataset images, helping in robust model development. Explore more about mosaicing and training techniques on our [Training](../../modes/train.md) page.
 
-### Why should I use Ultralytics YOLOv8 for object detection tasks?
+### Why should I use Ultralytics YOLO11 for object detection tasks?
 
-Ultralytics YOLOv8 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLOv8 documentation](https://github.com/ultralytics/ultralytics).
+Ultralytics YOLO11 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLO11 documentation](https://github.com/ultralytics/ultralytics).

docs/en/datasets/obb/index.md

@@ -39,8 +39,8 @@ To train a model using these OBB formats:
         ```python
         from ultralytics import YOLO
 
-        # Create a new YOLOv8n-OBB model from scratch
-        model = YOLO("yolov8n-obb.yaml")
+        # Create a new YOLO11n-OBB model from scratch
+        model = YOLO("yolo11n-obb.yaml")
 
         # Train the model on the DOTAv1 dataset
         results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
@@ -49,8 +49,8 @@ To train a model using these OBB formats:
     === "CLI"
 
         ```bash
-        # Train a new YOLOv8n-OBB model on the DOTAv1 dataset
-        yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=1024
+        # Train a new YOLO11n-OBB model on the DOTAv1 dataset
+        yolo obb train data=DOTAv1.yaml model=yolo11n-obb.pt epochs=100 imgsz=1024
         ```
 
 ## Supported Datasets
@@ -92,7 +92,7 @@ It's imperative to validate the compatibility of the dataset with your model and
 
 Oriented Bounding Boxes (OBB) are a type of bounding box annotation where the box can be rotated to align more closely with the object being detected, rather than just being axis-aligned. This is particularly useful in aerial or satellite imagery where objects might not be aligned with the image axes. In Ultralytics YOLO models, OBBs are represented by their four corner points in the YOLO OBB format. This allows for more accurate object detection since the bounding boxes can rotate to fit the objects better.
 
-### How do I convert my existing DOTA dataset labels to YOLO OBB format for use with Ultralytics YOLOv8?
+### How do I convert my existing DOTA dataset labels to YOLO OBB format for use with Ultralytics YOLO11?
 
 You can convert DOTA dataset labels to YOLO OBB format using the `convert_dota_to_yolo_obb` function from Ultralytics. This conversion ensures compatibility with the Ultralytics YOLO models, enabling you to leverage the OBB capabilities for enhanced object detection. Here's a quick example:
 
@@ -104,9 +104,9 @@ convert_dota_to_yolo_obb("path/to/DOTA")
 
 This script will reformat your DOTA annotations into a YOLO-compatible format.
 
-### How do I train a YOLOv8 model with oriented bounding boxes (OBB) on my dataset?
+### How do I train a YOLO11 model with oriented bounding boxes (OBB) on my dataset?
 
-Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO OBB format and then using the Ultralytics API to train the model. Here's an example in both Python and CLI:
+Training a YOLO11 model with OBBs involves ensuring your dataset is in the YOLO OBB format and then using the Ultralytics API to train the model. Here's an example in both Python and CLI:
 
 !!! example
 
@@ -115,8 +115,8 @@ Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO
         ```python
         from ultralytics import YOLO
 
-        # Create a new YOLOv8n-OBB model from scratch
-        model = YOLO("yolov8n-obb.yaml")
+        # Create a new YOLO11n-OBB model from scratch
+        model = YOLO("yolo11n-obb.yaml")
 
         # Train the model on the custom dataset
         results = model.train(data="your_dataset.yaml", epochs=100, imgsz=640)
@@ -125,8 +125,8 @@ Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO
     === "CLI"
 
         ```bash
-        # Train a new YOLOv8n-OBB model on the custom dataset
-        yolo obb train data=your_dataset.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
+        # Train a new YOLO11n-OBB model on the custom dataset
+        yolo obb train data=your_dataset.yaml model=yolo11n-obb.yaml epochs=100 imgsz=640
         ```
 
 This ensures your model leverages the detailed OBB annotations for improved detection [accuracy](https://www.ultralytics.com/glossary/accuracy).
@@ -142,6 +142,6 @@ Currently, Ultralytics supports the following datasets for OBB training:
 
 These datasets are tailored for scenarios where OBBs offer a significant advantage, such as aerial and satellite image analysis.
 
-### Can I use my own dataset with oriented bounding boxes for YOLOv8 training, and if so, how?
+### Can I use my own dataset with oriented bounding boxes for YOLO11 training, and if so, how?
 
-Yes, you can use your own dataset with oriented bounding boxes for YOLOv8 training. Ensure your dataset annotations are converted to the YOLO OBB format, which involves defining bounding boxes by their four corner points. You can then create a YAML configuration file specifying the dataset paths, classes, and other necessary details. For more information on creating and configuring your datasets, refer to the [Supported Datasets](#supported-datasets) section.
+Yes, you can use your own dataset with oriented bounding boxes for YOLO11 training. Ensure your dataset annotations are converted to the YOLO OBB format, which involves defining bounding boxes by their four corner points. You can then create a YAML configuration file specifying the dataset paths, classes, and other necessary details. For more information on creating and configuring your datasets, refer to the [Supported Datasets](#supported-datasets) section.

docs/en/datasets/pose/coco.md

@@ -14,12 +14,12 @@ The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialize
 
 | Model                                                                                                | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
 | ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
-| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt)       | 640                   | 50.4                  | 80.1               | 131.8                          | 1.18                                | 3.3                | 9.2               |
-| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-pose.pt)       | 640                   | 60.0                  | 86.2               | 233.2                          | 1.42                                | 11.6               | 30.2              |
-| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-pose.pt)       | 640                   | 65.0                  | 88.8               | 456.3                          | 2.00                                | 26.4               | 81.0              |
-| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-pose.pt)       | 640                   | 67.6                  | 90.0               | 784.5                          | 2.59                                | 44.4               | 168.6             |
-| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose.pt)       | 640                   | 69.2                  | 90.2               | 1607.1                         | 3.73                                | 69.4               | 263.2             |
-| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |
+| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt)       | 640                   | 50.4                  | 80.1               | 131.8                          | 1.18                                | 3.3                | 9.2               |
+| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt)       | 640                   | 60.0                  | 86.2               | 233.2                          | 1.42                                | 11.6               | 30.2              |
+| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt)       | 640                   | 65.0                  | 88.8               | 456.3                          | 2.00                                | 26.4               | 81.0              |
+| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt)       | 640                   | 67.6                  | 90.0               | 784.5                          | 2.59                                | 44.4               | 168.6             |
+| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt)       | 640                   | 69.2                  | 90.2               | 1607.1                         | 3.73                                | 69.4               | 263.2             |
+| [YOLO11x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |
 
 ## Key Features
 
@@ -51,7 +51,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-pose model on the COCO-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -61,7 +61,7 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 [epochs](https://
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
@@ -71,7 +71,7 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 [epochs](https://
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo pose train data=coco-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=coco-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -109,11 +109,11 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
 
 ### What is the COCO-Pose dataset and how is it used with Ultralytics YOLO for pose estimation?
 
-The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLOv8n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the [Training](../../modes/train.md) documentation.
+The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLO11n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the [Training](../../modes/train.md) documentation.
 
-### How can I train a YOLOv8 model on the COCO-Pose dataset?
+### How can I train a YOLO11 model on the COCO-Pose dataset?
 
-Training a YOLOv8 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLOv8n-pose model for 100 epochs with an image size of 640, you can follow the steps below:
+Training a YOLO11 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLO11n-pose model for 100 epochs with an image size of 640, you can follow the steps below:
 
 !!! example "Train Example"
 
@@ -123,7 +123,7 @@ Training a YOLOv8 model on the COCO-Pose dataset can be accomplished using eithe
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
@@ -133,14 +133,14 @@ Training a YOLOv8 model on the COCO-Pose dataset can be accomplished using eithe
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo pose train data=coco-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=coco-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
         ```
 
 For more details on the training process and available arguments, check the [training page](../../modes/train.md).
 
 ### What are the different metrics provided by the COCO-Pose dataset for evaluating model performance?
 
-The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the [accuracy](https://www.ultralytics.com/glossary/accuracy) of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLOv8n-pose, YOLOv8s-pose, and others have specific performance metrics listed in the documentation, like mAP<sup>pose</sup>50-95 and mAP<sup>pose</sup>50.
+The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the [accuracy](https://www.ultralytics.com/glossary/accuracy) of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLO11n-pose, YOLO11s-pose, and others have specific performance metrics listed in the documentation, like mAP<sup>pose</sup>50-95 and mAP<sup>pose</sup>50.
 
 ### How is the dataset structured and split for the COCO-Pose dataset?
 
@@ -154,6 +154,6 @@ These subsets help organize the training, validation, and testing phases effecti
 
 ### What are the key features and applications of the COCO-Pose dataset?
 
-The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLOv8n-pose) can significantly streamline the process ([Key Features](#key-features)).
+The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLO11n-pose) can significantly streamline the process ([Key Features](#key-features)).
 
 If you use the COCO-Pose dataset in your research or development work, please cite the paper with the following [BibTeX entry](#citations-and-acknowledgments).

docs/en/datasets/pose/coco8-pose.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the compact, versatile COCO8-Pose dataset for testing and debugging object detection models. Ideal for quick experiments with YOLOv8.
-keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOLOv8, machine learning, computer vision, training data
+description: Explore the compact, versatile COCO8-Pose dataset for testing and debugging object detection models. Ideal for quick experiments with YOLO11.
+keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOLO11, machine learning, computer vision, training data
 ---
 
 # COCO8-Pose Dataset
@@ -10,7 +10,7 @@ keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOL
 
 [Ultralytics](https://www.ultralytics.com/) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging [object detection](https://www.ultralytics.com/glossary/object-detection) models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
-This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
 
 ## Dataset YAML
 
@@ -24,7 +24,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -34,7 +34,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 [epochs](https:/
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
@@ -44,7 +44,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 [epochs](https:/
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -80,13 +80,13 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
 
 ## FAQ
 
-### What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLOv8?
+### What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLO11?
 
-The COCO8-Pose dataset is a small, versatile pose detection dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/). For more details on dataset configuration, check out the dataset YAML file [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
+The COCO8-Pose dataset is a small, versatile pose detection dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with [Ultralytics YOLO11](https://docs.ultralytics.com/models/yolo11/). For more details on dataset configuration, check out the dataset YAML file [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
 
-### How do I train a YOLOv8 model using the COCO8-Pose dataset in Ultralytics?
+### How do I train a YOLO11 model using the COCO8-Pose dataset in Ultralytics?
 
-To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, follow these examples:
+To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, follow these examples:
 
 !!! example "Train Example"
 
@@ -96,7 +96,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-pose.pt")
+        model = YOLO("yolo11n-pose.pt")
 
         # Train the model
         results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
@@ -105,7 +105,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
     === "CLI"
 
         ```bash
-        yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
         ```
 
 For a comprehensive list of training arguments, refer to the model [Training](../../modes/train.md) page.
@@ -120,12 +120,12 @@ The COCO8-Pose dataset offers several benefits:
 
 For more about its features and usage, see the [Dataset Introduction](#introduction) section.
 
-### How does mosaicing benefit the YOLOv8 training process using the COCO8-Pose dataset?
+### How does mosaicing benefit the YOLO11 training process using the COCO8-Pose dataset?
 
 Mosaicing, demonstrated in the sample images of the COCO8-Pose dataset, combines multiple images into one, increasing the variety of objects and scenes within each training batch. This technique helps improve the model's ability to generalize across various object sizes, aspect ratios, and contexts, ultimately enhancing model performance. See the [Sample Images and Annotations](#sample-images-and-annotations) section for example images.
 
 ### Where can I find the COCO8-Pose dataset YAML file and how do I use it?
 
-The COCO8-Pose dataset YAML file can be found [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml). This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLOv8 training scripts as mentioned in the [Train Example](#how-do-i-train-a-yolov8-model-using-the-coco8-pose-dataset-in-ultralytics) section.
+The COCO8-Pose dataset YAML file can be found [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml). This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLO11 training scripts as mentioned in the [Train Example](#how-do-i-train-a-yolo11-model-using-the-coco8-pose-dataset-in-ultralytics) section.
 
 For more FAQs and detailed documentation, visit the [Ultralytics Documentation](https://docs.ultralytics.com/).

docs/en/datasets/pose/hand-keypoints.md

@@ -8,7 +8,7 @@ keywords: Hand KeyPoints, pose estimation, dataset, keypoints, MediaPipe, YOLO,
 
 ## Introduction
 
-The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high accuracy and consistency, and the dataset is compatible [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) formats.
+The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high accuracy and consistency, and the dataset is compatible [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) formats.
 
 ## Hand Landmarks
 
@@ -30,7 +30,7 @@ Each hand has a total of 21 keypoints.
 ## Key Features
 
 - **Large Dataset**: 26,768 images with hand keypoint annotations.
-- **YOLOv8 Compatibility**: Ready for use with YOLOv8 models.
+- **YOLO11 Compatibility**: Ready for use with YOLO11 models.
 - **21 Keypoints**: Detailed hand pose representation.
 
 ## Dataset Structure
@@ -56,7 +56,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-pose model on the Hand Keypoints dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-pose model on the Hand Keypoints dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -66,7 +66,7 @@ To train a YOLOv8n-pose model on the Hand Keypoints dataset for 100 [epochs](htt
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)
@@ -76,7 +76,7 @@ To train a YOLOv8n-pose model on the Hand Keypoints dataset for 100 [epochs](htt
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo pose train data=hand-keypoints.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=hand-keypoints.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -109,9 +109,9 @@ We would also like to acknowledge the creator of this dataset, [Rion Dsilva](htt
 
 ## FAQ
 
-### How do I train a YOLOv8 model on the Hand Keypoints dataset?
+### How do I train a YOLO11 model on the Hand Keypoints dataset?
 
-To train a YOLOv8 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLOv8n-pose model for 100 epochs with an image size of 640:
+To train a YOLO11 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLO11n-pose model for 100 epochs with an image size of 640:
 
 !!! Example
 
@@ -121,7 +121,7 @@ To train a YOLOv8 model on the Hand Keypoints dataset, you can use either Python
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)
@@ -131,7 +131,7 @@ To train a YOLOv8 model on the Hand Keypoints dataset, you can use either Python
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo pose train data=hand-keypoints.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=hand-keypoints.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
         ```
 
 For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
@@ -141,7 +141,7 @@ For a comprehensive list of available arguments, refer to the model [Training](.
 The Hand Keypoints dataset is designed for advanced pose estimation tasks and includes several key features:
 
 - **Large Dataset**: Contains 26,768 images with hand keypoint annotations.
-- **YOLOv8 Compatibility**: Ready for use with YOLOv8 models.
+- **YOLO11 Compatibility**: Ready for use with YOLO11 models.
 - **21 Keypoints**: Detailed hand pose representation, including wrist and finger joints.
 
 For more details, you can explore the [Hand Keypoints Dataset](#introduction) section.

docs/en/datasets/pose/index.md

@@ -72,7 +72,7 @@ The `train` and `val` fields specify the paths to the directories containing the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
@@ -82,7 +82,7 @@ The `train` and `val` fields specify the paths to the directories containing the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
         ```
 
 ## Supported Datasets
@@ -171,7 +171,7 @@ To use the COCO-Pose dataset with Ultralytics YOLO:
     ```python
     from ultralytics import YOLO
 
-    model = YOLO("yolov8n-pose.pt")  # load pretrained model
+    model = YOLO("yolo11n-pose.pt")  # load pretrained model
     results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
     ```
 
@@ -188,7 +188,7 @@ To add your dataset:
     ```python
     from ultralytics import YOLO
 
-    model = YOLO("yolov8n-pose.pt")
+    model = YOLO("yolo11n-pose.pt")
     results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640)
     ```
 

docs/en/datasets/pose/tiger-pose.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Explore Ultralytics Tiger-Pose dataset with 263 diverse images. Ideal for testing, training, and refining pose estimation algorithms.
-keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLOv8, training data, machine learning, neural networks
+keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLO11, training data, machine learning, neural networks
 ---
 
 # Tiger-Pose Dataset
@@ -12,7 +12,7 @@ keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLOv8, training da
 
 Despite its manageable size of 210 images, tiger-pose dataset offers diversity, making it suitable for assessing training pipelines, identifying potential errors, and serving as a valuable preliminary step before working with larger datasets for pose estimation.
 
-This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
+This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
 
 <p align="center">
   <br>
@@ -22,7 +22,7 @@ This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Train YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB
+  <strong>Watch:</strong> Train YOLO11 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB
 </p>
 
 ## Dataset YAML
@@ -37,7 +37,7 @@ A YAML (Yet Another Markup Language) file serves as the means to specify the con
 
 ## Usage
 
-To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -47,7 +47,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 [epochs](https:/
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
@@ -57,7 +57,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 [epochs](https:/
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo task=pose mode=train data=tiger-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -101,11 +101,11 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
 
 ### What is the Ultralytics Tiger-Pose dataset used for?
 
-The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
+The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
 
-### How do I train a YOLOv8 model on the Tiger-Pose dataset?
+### How do I train a YOLO11 model on the Tiger-Pose dataset?
 
-To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the [Training](../../modes/train.md) page:
+To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the [Training](../../modes/train.md) page:
 
 !!! example "Train Example"
 
@@ -115,7 +115,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
@@ -126,16 +126,16 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo task=pose mode=train data=tiger-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
         ```
 
 ### What configurations does the `tiger-pose.yaml` file include?
 
 The `tiger-pose.yaml` file is used to specify the configuration details of the Tiger-Pose dataset. It includes crucial data such as file paths and class definitions. To see the exact configuration, you can check out the [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).
 
-### How can I run inference using a YOLOv8 model trained on the Tiger-Pose dataset?
+### How can I run inference using a YOLO11 model trained on the Tiger-Pose dataset?
 
-To perform inference using a YOLOv8 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the [Prediction](../../modes/predict.md) page:
+To perform inference using a YOLO11 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the [Prediction](../../modes/predict.md) page:
 
 !!! example "Inference Example"
 
@@ -161,4 +161,4 @@ To perform inference using a YOLOv8 model trained on the Tiger-Pose dataset, you
 
 ### What are the benefits of using the Tiger-Pose dataset for pose estimation?
 
-The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics), enhancing model performance and [accuracy](https://www.ultralytics.com/glossary/accuracy).
+The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics), enhancing model performance and [accuracy](https://www.ultralytics.com/glossary/accuracy).

docs/en/datasets/segment/carparts-seg.md

@@ -45,7 +45,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train Ultralytics YOLO11n model on the Carparts Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -55,7 +55,7 @@ To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
@@ -65,7 +65,7 @@ To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=carparts-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=carparts-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -108,9 +108,9 @@ We extend our thanks to the Roboflow team for their dedication in developing and
 
 The [Roboflow Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics) is a curated collection of images and videos specifically designed for car part segmentation tasks in computer vision. This dataset includes a diverse range of visuals captured from multiple perspectives, making it an invaluable resource for training and testing segmentation models for automotive applications.
 
-### How can I use the Carparts Segmentation Dataset with Ultralytics YOLOv8?
+### How can I use the Carparts Segmentation Dataset with Ultralytics YOLO11?
 
-To train a YOLOv8 model on the Carparts Segmentation dataset, you can follow these steps:
+To train a YOLO11 model on the Carparts Segmentation dataset, you can follow these steps:
 
 !!! example "Train Example"
 
@@ -120,7 +120,7 @@ To train a YOLOv8 model on the Carparts Segmentation dataset, you can follow the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
@@ -130,7 +130,7 @@ To train a YOLOv8 model on the Carparts Segmentation dataset, you can follow the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=carparts-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=carparts-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 For more details, refer to the [Training](../../modes/train.md) documentation.

docs/en/datasets/segment/coco.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Explore the COCO-Seg dataset, an extension of COCO, with detailed segmentation annotations. Learn how to train YOLO models with COCO-Seg.
-keywords: COCO-Seg, dataset, YOLO models, instance segmentation, object detection, COCO dataset, YOLOv8, computer vision, Ultralytics, machine learning
+keywords: COCO-Seg, dataset, YOLO models, instance segmentation, object detection, COCO dataset, YOLO11, computer vision, Ultralytics, machine learning
 ---
 
 # COCO-Seg Dataset
@@ -12,11 +12,11 @@ The [COCO-Seg](https://cocodataset.org/#home) dataset, an extension of the COCO
 
 | Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
 | -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
-| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
-| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
-| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
-| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
-| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
+| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
+| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
+| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
+| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
+| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
 
 ## Key Features
 
@@ -49,7 +49,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-seg model on the COCO-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -59,7 +59,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 [epochs](https://ww
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco-seg.yaml", epochs=100, imgsz=640)
@@ -69,7 +69,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 [epochs](https://ww
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -109,9 +109,9 @@ We extend our thanks to the COCO Consortium for creating and maintaining this in
 
 The [COCO-Seg](https://cocodataset.org/#home) dataset is an extension of the original COCO (Common Objects in Context) dataset, specifically designed for instance segmentation tasks. While it uses the same images as the COCO dataset, COCO-Seg includes more detailed segmentation annotations, making it a powerful resource for researchers and developers focusing on object instance segmentation.
 
-### How can I train a YOLOv8 model using the COCO-Seg dataset?
+### How can I train a YOLO11 model using the COCO-Seg dataset?
 
-To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -121,7 +121,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco-seg.yaml", epochs=100, imgsz=640)
@@ -131,7 +131,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ### What are the key features of the COCO-Seg dataset?
@@ -145,15 +145,15 @@ The COCO-Seg dataset includes several key features:
 
 ### What pretrained models are available for COCO-Seg, and what are their performance metrics?
 
-The COCO-Seg dataset supports multiple pretrained YOLOv8 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:
+The COCO-Seg dataset supports multiple pretrained YOLO11 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:
 
 | Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
 | -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
-| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
-| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
-| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
-| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
-| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
+| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
+| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
+| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
+| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
+| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
 
 ### How is the COCO-Seg dataset structured and what subsets does it contain?
 

docs/en/datasets/segment/coco8-seg.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Discover the versatile and manageable COCO8-Seg dataset by Ultralytics, ideal for testing and debugging segmentation models or new detection approaches.
-keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLOv8, COCO 2017, model training, computer vision, dataset configuration
+keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLO11, COCO 2017, model training, computer vision, dataset configuration
 ---
 
 # COCO8-Seg Dataset
@@ -10,7 +10,7 @@ keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLOv8, COCO 2017, model
 
 [Ultralytics](https://www.ultralytics.com/) COCO8-Seg is a small, but versatile [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
-This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
 
 ## Dataset YAML
 
@@ -24,7 +24,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLO11n-seg model on the COCO8-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -34,7 +34,7 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 [epochs](https://w
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
@@ -44,7 +44,7 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 [epochs](https://w
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco8-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -80,13 +80,13 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
 
 ## FAQ
 
-### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLOv8?
+### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLO11?
 
-The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLOv8](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
+The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLO11](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
 
-### How can I train a YOLOv8n-seg model using the COCO8-Seg dataset?
+### How can I train a YOLO11n-seg model using the COCO8-Seg dataset?
 
-To train a **YOLOv8n-seg** model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
+To train a **YOLO11n-seg** model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
 
 !!! example "Train Example"
 
@@ -96,7 +96,7 @@ To train a **YOLOv8n-seg** model on the COCO8-Seg dataset for 100 epochs with an
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # Load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # Load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
@@ -106,7 +106,7 @@ To train a **YOLOv8n-seg** model on the COCO8-Seg dataset for 100 epochs with an
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco8-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 For a thorough explanation of available arguments and configuration options, you can check the [Training](../../modes/train.md) documentation.

docs/en/datasets/segment/crack-seg.md

@@ -34,7 +34,7 @@ A YAML (Yet Another Markup Language) file is employed to outline the configurati
 
 ## Usage
 
-To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train Ultralytics YOLO11n model on the Crack Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -44,7 +44,7 @@ To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 [ep
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="crack-seg.yaml", epochs=100, imgsz=640)
@@ -54,7 +54,7 @@ To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 [ep
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=crack-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=crack-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -98,9 +98,9 @@ We would like to acknowledge the Roboflow team for creating and maintaining the
 
 The [Roboflow Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) is a comprehensive collection of 4029 static images designed specifically for transportation and public safety studies. It is ideal for tasks such as self-driving car model development and infrastructure maintenance. The dataset includes training, testing, and validation sets, aiding in accurate crack detection and segmentation.
 
-### How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLOv8?
+### How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLO11?
 
-To train an Ultralytics YOLOv8 model on the Crack Segmentation dataset, use the following code snippets. Detailed instructions and further parameters can be found on the model [Training](../../modes/train.md) page.
+To train an Ultralytics YOLO11 model on the Crack Segmentation dataset, use the following code snippets. Detailed instructions and further parameters can be found on the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -110,7 +110,7 @@ To train an Ultralytics YOLOv8 model on the Crack Segmentation dataset, use the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="crack-seg.yaml", epochs=100, imgsz=640)
@@ -120,7 +120,7 @@ To train an Ultralytics YOLOv8 model on the Crack Segmentation dataset, use the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=crack-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=crack-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ### Why should I use the Crack Segmentation Dataset for my self-driving car project?

docs/en/datasets/segment/index.md

@@ -74,7 +74,7 @@ The `train` and `val` fields specify the paths to the directories containing the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
@@ -84,7 +84,7 @@ The `train` and `val` fields specify the paths to the directories containing the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco8-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Supported Datasets
@@ -137,13 +137,13 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
         ```python
         from ultralytics.data.annotator import auto_annotate
 
-        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt")
+        auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam_b.pt")
         ```
 
 | Argument     | Type                    | Description                                                                                                 | Default        |
 | ------------ | ----------------------- | ----------------------------------------------------------------------------------------------------------- | -------------- |
 | `data`       | `str`                   | Path to a folder containing images to be annotated.                                                         | `None`         |
-| `det_model`  | `str, optional`         | Pre-trained YOLO detection model. Defaults to `'yolov8x.pt'`.                                               | `'yolov8x.pt'` |
+| `det_model`  | `str, optional`         | Pre-trained YOLO detection model. Defaults to `'yolo11x.pt'`.                                               | `'yolo11x.pt'` |
 | `sam_model`  | `str, optional`         | Pre-trained SAM segmentation model. Defaults to `'sam_b.pt'`.                                               | `'sam_b.pt'`   |
 | `device`     | `str, optional`         | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                        | `''`           |
 | `output_dir` | `str or None, optional` | Directory to save the annotated results. Defaults to a `'labels'` folder in the same directory as `'data'`. | `None`         |
@@ -195,7 +195,7 @@ Auto-annotation in Ultralytics YOLO allows you to generate segmentation annotati
 ```python
 from ultralytics.data.annotator import auto_annotate
 
-auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt")
+auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam_b.pt")
 ```
 
 This function automates the annotation process, making it faster and more efficient. For more details, explore the [Auto-Annotation](#auto-annotation) section.

docs/en/datasets/segment/package-seg.md

@@ -34,7 +34,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train Ultralytics YOLOv8n model on the Package Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train Ultralytics YOLO11n model on the Package Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -44,7 +44,7 @@ To train Ultralytics YOLOv8n model on the Package Segmentation dataset for 100 [
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
         results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)
@@ -54,7 +54,7 @@ To train Ultralytics YOLOv8n model on the Package Segmentation dataset for 100 [
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=package-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=package-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations
@@ -97,9 +97,9 @@ We express our gratitude to the Roboflow team for their efforts in creating and
 
 The [Roboflow Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics) is a curated collection of images tailored for tasks involving package segmentation. It includes diverse images of packages in various contexts, making it invaluable for training and evaluating segmentation models. This dataset is particularly useful for applications in logistics, warehouse automation, and any project requiring precise package analysis. It helps optimize logistics and enhance vision models for accurate package identification and sorting.
 
-### How do I train an Ultralytics YOLOv8 model on the Package Segmentation Dataset?
+### How do I train an Ultralytics YOLO11 model on the Package Segmentation Dataset?
 
-You can train an Ultralytics YOLOv8n model using both Python and CLI methods. Use the snippets below:
+You can train an Ultralytics YOLO11n model using both Python and CLI methods. Use the snippets below:
 
 !!! example "Train Example"
 
@@ -109,7 +109,7 @@ You can train an Ultralytics YOLOv8n model using both Python and CLI methods. Us
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n-seg.pt")  # load a pretrained model
+        model = YOLO("yolo11n-seg.pt")  # load a pretrained model
 
         # Train the model
         results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)
@@ -119,7 +119,7 @@ You can train an Ultralytics YOLOv8n model using both Python and CLI methods. Us
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=package-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=package-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 Refer to the model [Training](../../modes/train.md) page for more details.
@@ -134,9 +134,9 @@ The dataset is structured into three main components:
 
 This structure ensures a balanced dataset for thorough model training, validation, and testing, enhancing the performance of segmentation algorithms.
 
-### Why should I use Ultralytics YOLOv8 with the Package Segmentation Dataset?
+### Why should I use Ultralytics YOLO11 with the Package Segmentation Dataset?
 
-Ultralytics YOLOv8 provides state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed for real-time object detection and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLOv8's capabilities for precise package segmentation. This combination is especially beneficial for industries like logistics and warehouse automation, where accurate package identification is critical. For more information, check out our [page on YOLOv8 segmentation](https://docs.ultralytics.com/models/yolov8/).
+Ultralytics YOLO11 provides state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed for real-time object detection and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLO11's capabilities for precise package segmentation. This combination is especially beneficial for industries like logistics and warehouse automation, where accurate package identification is critical. For more information, check out our [page on YOLO11 segmentation](https://docs.ultralytics.com/models/yolo11/).
 
 ### How can I access and use the package-seg.yaml file for the Package Segmentation Dataset?
 

docs/en/datasets/track/index.md

@@ -19,14 +19,14 @@ Multi-Object Detector doesn't need standalone training and directly supports pre
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
         ```
 
     === "CLI"
 
         ```bash
-        yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show
+        yolo track model=yolo11n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show
         ```
 
 ## FAQ
@@ -42,17 +42,17 @@ To use Multi-Object Tracking with Ultralytics YOLO, you can start by using the P
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8n.pt")  # Load the YOLOv8 model
+        model = YOLO("yolo11n.pt")  # Load the YOLO11 model
         results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
         ```
 
     === "CLI"
 
         ```bash
-        yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3 iou=0.5 show
+        yolo track model=yolo11n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3 iou=0.5 show
         ```
 
-These commands load the YOLOv8 model and use it for tracking objects in the given video source with specific confidence (`conf`) and [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (`iou`) thresholds. For more details, refer to the [track mode documentation](../../modes/track.md).
+These commands load the YOLO11 model and use it for tracking objects in the given video source with specific confidence (`conf`) and [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (`iou`) thresholds. For more details, refer to the [track mode documentation](../../modes/track.md).
 
 ### What are the upcoming features for training trackers in Ultralytics?
 

docs/en/guides/analytics.md

@@ -1,10 +1,10 @@
 ---
 comments: true
 description: Learn to create line graphs, bar plots, and pie charts using Python with guided instructions and code snippets. Maximize your data visualization skills!.
-keywords: Ultralytics, YOLOv8, data visualization, line graphs, bar plots, pie charts, Python, analytics, tutorial, guide
+keywords: Ultralytics, YOLO11, data visualization, line graphs, bar plots, pie charts, Python, analytics, tutorial, guide
 ---
 
-# Analytics using Ultralytics YOLOv8
+# Analytics using Ultralytics YOLO11
 
 ## Introduction
 
@@ -42,7 +42,7 @@ This guide provides a comprehensive overview of three fundamental types of [data
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8s.pt")
+        model = YOLO("yolo11n.pt")
 
         cap = cv2.VideoCapture("Path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
@@ -91,7 +91,7 @@ This guide provides a comprehensive overview of three fundamental types of [data
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8s.pt")
+        model = YOLO("yolo11n.pt")
 
         cap = cv2.VideoCapture("Path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
@@ -152,7 +152,7 @@ This guide provides a comprehensive overview of three fundamental types of [data
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8s.pt")
+        model = YOLO("yolo11n.pt")
 
         cap = cv2.VideoCapture("Path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
@@ -202,7 +202,7 @@ This guide provides a comprehensive overview of three fundamental types of [data
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8s.pt")
+        model = YOLO("yolo11n.pt")
 
         cap = cv2.VideoCapture("Path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
@@ -252,7 +252,7 @@ This guide provides a comprehensive overview of three fundamental types of [data
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8s.pt")
+        model = YOLO("yolo11n.pt")
 
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
@@ -330,11 +330,11 @@ Understanding when and how to use different types of visualizations is crucial f
 
 ## FAQ
 
-### How do I create a line graph using Ultralytics YOLOv8 Analytics?
+### How do I create a line graph using Ultralytics YOLO11 Analytics?
 
-To create a line graph using Ultralytics YOLOv8 Analytics, follow these steps:
+To create a line graph using Ultralytics YOLO11 Analytics, follow these steps:
 
-1. Load a YOLOv8 model and open your video file.
+1. Load a YOLO11 model and open your video file.
 2. Initialize the `Analytics` class with the type set to "line."
 3. Iterate through video frames, updating the line graph with relevant data, such as object counts per frame.
 4. Save the output video displaying the line graph.
@@ -346,7 +346,7 @@ import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8s.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("Path/to/video/file.mp4")
 out = cv2.VideoWriter("line_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
 
@@ -366,11 +366,11 @@ out.release()
 cv2.destroyAllWindows()
 ```
 
-For further details on configuring the `Analytics` class, visit the [Analytics using Ultralytics YOLOv8 📊](#analytics-using-ultralytics-yolov8) section.
+For further details on configuring the `Analytics` class, visit the [Analytics using Ultralytics YOLO11 📊](#analytics-using-ultralytics-yolo11) section.
 
-### What are the benefits of using Ultralytics YOLOv8 for creating bar plots?
+### What are the benefits of using Ultralytics YOLO11 for creating bar plots?
 
-Using Ultralytics YOLOv8 for creating bar plots offers several benefits:
+Using Ultralytics YOLO11 for creating bar plots offers several benefits:
 
 1. **Real-time Data Visualization**: Seamlessly integrate [object detection](https://www.ultralytics.com/glossary/object-detection) results into bar plots for dynamic updates.
 2. **Ease of Use**: Simple API and functions make it straightforward to implement and visualize data.
@@ -384,7 +384,7 @@ import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8s.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("Path/to/video/file.mp4")
 out = cv2.VideoWriter("bar_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
 
@@ -409,9 +409,9 @@ cv2.destroyAllWindows()
 
 To learn more, visit the [Bar Plot](#visual-samples) section in the guide.
 
-### Why should I use Ultralytics YOLOv8 for creating pie charts in my data visualization projects?
+### Why should I use Ultralytics YOLO11 for creating pie charts in my data visualization projects?
 
-Ultralytics YOLOv8 is an excellent choice for creating pie charts because:
+Ultralytics YOLO11 is an excellent choice for creating pie charts because:
 
 1. **Integration with Object Detection**: Directly integrate object detection results into pie charts for immediate insights.
 2. **User-Friendly API**: Simple to set up and use with minimal code.
@@ -425,7 +425,7 @@ import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8s.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("Path/to/video/file.mp4")
 out = cv2.VideoWriter("pie_chart.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
 
@@ -450,9 +450,9 @@ cv2.destroyAllWindows()
 
 For more information, refer to the [Pie Chart](#visual-samples) section in the guide.
 
-### Can Ultralytics YOLOv8 be used to track objects and dynamically update visualizations?
+### Can Ultralytics YOLO11 be used to track objects and dynamically update visualizations?
 
-Yes, Ultralytics YOLOv8 can be used to track objects and dynamically update visualizations. It supports tracking multiple objects in real-time and can update various visualizations like line graphs, bar plots, and pie charts based on the tracked objects' data.
+Yes, Ultralytics YOLO11 can be used to track objects and dynamically update visualizations. It supports tracking multiple objects in real-time and can update various visualizations like line graphs, bar plots, and pie charts based on the tracked objects' data.
 
 Example for tracking and updating a line graph:
 
@@ -461,7 +461,7 @@ import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8s.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("Path/to/video/file.mp4")
 out = cv2.VideoWriter("line_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
 
@@ -483,11 +483,11 @@ cv2.destroyAllWindows()
 
 To learn about the complete functionality, see the [Tracking](../modes/track.md) section.
 
-### What makes Ultralytics YOLOv8 different from other object detection solutions like [OpenCV](https://www.ultralytics.com/glossary/opencv) and [TensorFlow](https://www.ultralytics.com/glossary/tensorflow)?
+### What makes Ultralytics YOLO11 different from other object detection solutions like [OpenCV](https://www.ultralytics.com/glossary/opencv) and [TensorFlow](https://www.ultralytics.com/glossary/tensorflow)?
 
-Ultralytics YOLOv8 stands out from other object detection solutions like OpenCV and TensorFlow for multiple reasons:
+Ultralytics YOLO11 stands out from other object detection solutions like OpenCV and TensorFlow for multiple reasons:
 
-1. **State-of-the-art [Accuracy](https://www.ultralytics.com/glossary/accuracy)**: YOLOv8 provides superior accuracy in object detection, segmentation, and classification tasks.
+1. **State-of-the-art [Accuracy](https://www.ultralytics.com/glossary/accuracy)**: YOLO11 provides superior accuracy in object detection, segmentation, and classification tasks.
 2. **Ease of Use**: User-friendly API allows for quick implementation and integration without extensive coding.
 3. **Real-time Performance**: Optimized for high-speed inference, suitable for real-time applications.
 4. **Diverse Applications**: Supports various tasks including multi-object tracking, custom model training, and exporting to different formats like ONNX, TensorRT, and CoreML.

docs/en/guides/azureml-quickstart.md

@@ -1,10 +1,10 @@
 ---
 comments: true
-description: Learn how to run YOLOv8 on AzureML. Quickstart instructions for terminal and notebooks to harness Azure's cloud computing for efficient model training.
-keywords: YOLOv8, AzureML, machine learning, cloud computing, quickstart, terminal, notebooks, model training, Python SDK, AI, Ultralytics
+description: Learn how to run YOLO11 on AzureML. Quickstart instructions for terminal and notebooks to harness Azure's cloud computing for efficient model training.
+keywords: YOLO11, AzureML, machine learning, cloud computing, quickstart, terminal, notebooks, model training, Python SDK, AI, Ultralytics
 ---
 
-# YOLOv8 🚀 on AzureML
+# YOLO11 🚀 on AzureML
 
 ## What is Azure?
 
@@ -22,7 +22,7 @@ For users of YOLO (You Only Look Once), AzureML provides a robust, scalable, and
 - Utilize built-in tools for data preprocessing, feature selection, and model training.
 - Collaborate more efficiently with capabilities for MLOps (Machine Learning Operations), including but not limited to monitoring, auditing, and versioning of models and data.
 
-In the subsequent sections, you will find a quickstart guide detailing how to run YOLOv8 object detection models using AzureML, either from a compute terminal or a notebook.
+In the subsequent sections, you will find a quickstart guide detailing how to run YOLO11 object detection models using AzureML, either from a compute terminal or a notebook.
 
 ## Prerequisites
 
@@ -49,8 +49,8 @@ Start your compute and open a Terminal:
 Create your conda virtualenv and install pip in it:
 
 ```bash
-conda create --name yolov8env -y
-conda activate yolov8env
+conda create --name yolo11env -y
+conda activate yolo11env
 conda install pip -y
 ```
 
@@ -63,18 +63,18 @@ pip install ultralytics
 pip install onnx>=1.12.0
 ```
 
-### Perform YOLOv8 tasks
+### Perform YOLO11 tasks
 
 Predict:
 
 ```bash
-yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
 ```
 
 Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01:
 
 ```bash
-yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
+yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
 ```
 
 You can find more [instructions to use the Ultralytics CLI here](../quickstart.md#use-ultralytics-with-cli).
@@ -92,11 +92,11 @@ Open the compute Terminal.
 From your compute terminal, you need to create a new ipykernel that will be used by your notebook to manage your dependencies:
 
 ```bash
-conda create --name yolov8env -y
-conda activate yolov8env
+conda create --name yolo11env -y
+conda activate yolo11env
 conda install pip -y
 conda install ipykernel -y
-python -m ipykernel install --user --name yolov8env --display-name "yolov8env"
+python -m ipykernel install --user --name yolo11env --display-name "yolo11env"
 ```
 
 Close your terminal and create a new notebook. From your Notebook, you can select the new kernel.
@@ -105,21 +105,21 @@ Then you can open a Notebook cell and install the required dependencies:
 
 ```bash
 %%bash
-source activate yolov8env
+source activate yolo11env
 cd ultralytics
 pip install -r requirements.txt
 pip install ultralytics
 pip install onnx>=1.12.0
 ```
 
-Note that we need to use the `source activate yolov8env` for all the %%bash cells, to make sure that the %%bash cell uses environment we want.
+Note that we need to use the `source activate yolo11env` for all the %%bash cells, to make sure that the %%bash cell uses environment we want.
 
 Run some predictions using the [Ultralytics CLI](../quickstart.md#use-ultralytics-with-cli):
 
 ```bash
 %%bash
-source activate yolov8env
-yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+source activate yolo11env
+yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
 ```
 
 Or with the [Ultralytics Python interface](../quickstart.md#use-ultralytics-with-python), for example to train the model:
@@ -128,7 +128,7 @@ Or with the [Ultralytics Python interface](../quickstart.md#use-ultralytics-with
 from ultralytics import YOLO
 
 # Load a model
-model = YOLO("yolov8n.pt")  # load an official YOLOv8n model
+model = YOLO("yolo11n.pt")  # load an official YOLO11n model
 
 # Use the model
 model.train(data="coco8.yaml", epochs=3)  # train the model
@@ -137,47 +137,47 @@ results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
 path = model.export(format="onnx")  # export the model to ONNX format
 ```
 
-You can use either the Ultralytics CLI or Python interface for running YOLOv8 tasks, as described in the terminal section above.
+You can use either the Ultralytics CLI or Python interface for running YOLO11 tasks, as described in the terminal section above.
 
-By following these steps, you should be able to get YOLOv8 running quickly on AzureML for quick trials. For more advanced uses, you may refer to the full AzureML documentation linked at the beginning of this guide.
+By following these steps, you should be able to get YOLO11 running quickly on AzureML for quick trials. For more advanced uses, you may refer to the full AzureML documentation linked at the beginning of this guide.
 
 ## Explore More with AzureML
 
-This guide serves as an introduction to get you up and running with YOLOv8 on AzureML. However, it only scratches the surface of what AzureML can offer. To delve deeper and unlock the full potential of AzureML for your machine learning projects, consider exploring the following resources:
+This guide serves as an introduction to get you up and running with YOLO11 on AzureML. However, it only scratches the surface of what AzureML can offer. To delve deeper and unlock the full potential of AzureML for your machine learning projects, consider exploring the following resources:
 
 - [Create a Data Asset](https://learn.microsoft.com/azure/machine-learning/how-to-create-data-assets): Learn how to set up and manage your data assets effectively within the AzureML environment.
 - [Initiate an AzureML Job](https://learn.microsoft.com/azure/machine-learning/how-to-train-model): Get a comprehensive understanding of how to kickstart your machine learning training jobs on AzureML.
 - [Register a Model](https://learn.microsoft.com/azure/machine-learning/how-to-manage-models): Familiarize yourself with model management practices including registration, versioning, and deployment.
-- [Train YOLOv8 with AzureML Python SDK](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azure-machine-learning-python-sdk-8268696be8ba): Explore a step-by-step guide on using the AzureML Python SDK to train your YOLOv8 models.
-- [Train YOLOv8 with AzureML CLI](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azureml-and-the-az-cli-73d3c870ba8e): Discover how to utilize the command-line interface for streamlined training and management of YOLOv8 models on AzureML.
+- [Train YOLO11 with AzureML Python SDK](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azure-machine-learning-python-sdk-8268696be8ba): Explore a step-by-step guide on using the AzureML Python SDK to train your YOLO11 models.
+- [Train YOLO11 with AzureML CLI](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azureml-and-the-az-cli-73d3c870ba8e): Discover how to utilize the command-line interface for streamlined training and management of YOLO11 models on AzureML.
 
 ## FAQ
 
-### How do I run YOLOv8 on AzureML for model training?
+### How do I run YOLO11 on AzureML for model training?
 
-Running YOLOv8 on AzureML for model training involves several steps:
+Running YOLO11 on AzureML for model training involves several steps:
 
 1. **Create a Compute Instance**: From your AzureML workspace, navigate to Compute > Compute instances > New, and select the required instance.
 
 2. **Setup Environment**: Start your compute instance, open a terminal, and create a conda environment:
 
     ```bash
-    conda create --name yolov8env -y
-    conda activate yolov8env
+    conda create --name yolo11env -y
+    conda activate yolo11env
     conda install pip -y
     pip install ultralytics onnx>=1.12.0
     ```
 
-3. **Run YOLOv8 Tasks**: Use the Ultralytics CLI to train your model:
+3. **Run YOLO11 Tasks**: Use the Ultralytics CLI to train your model:
     ```bash
-    yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
+    yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
     ```
 
 For more details, you can refer to the [instructions to use the Ultralytics CLI](../quickstart.md#use-ultralytics-with-cli).
 
-### What are the benefits of using AzureML for YOLOv8 training?
+### What are the benefits of using AzureML for YOLO11 training?
 
-AzureML provides a robust and efficient ecosystem for training YOLOv8 models:
+AzureML provides a robust and efficient ecosystem for training YOLO11 models:
 
 - **Scalability**: Easily scale your compute resources as your data and model complexity grows.
 - **MLOps Integration**: Utilize features like versioning, monitoring, and auditing to streamline ML operations.
@@ -185,9 +185,9 @@ AzureML provides a robust and efficient ecosystem for training YOLOv8 models:
 
 These advantages make AzureML an ideal platform for projects ranging from quick prototypes to large-scale deployments. For more tips, check out [AzureML Jobs](https://learn.microsoft.com/azure/machine-learning/how-to-train-model).
 
-### How do I troubleshoot common issues when running YOLOv8 on AzureML?
+### How do I troubleshoot common issues when running YOLO11 on AzureML?
 
-Troubleshooting common issues with YOLOv8 on AzureML can involve the following steps:
+Troubleshooting common issues with YOLO11 on AzureML can involve the following steps:
 
 - **Dependency Issues**: Ensure all required packages are installed. Refer to the `requirements.txt` file for dependencies.
 - **Environment Setup**: Verify that your conda environment is correctly activated before running commands.
@@ -202,7 +202,7 @@ Yes, AzureML allows you to use both the Ultralytics CLI and the Python interface
 - **CLI**: Ideal for quick tasks and running standard scripts directly from the terminal.
 
     ```bash
-    yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+    yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
     ```
 
 - **Python Interface**: Useful for more complex tasks requiring custom coding and integration within notebooks.
@@ -210,18 +210,18 @@ Yes, AzureML allows you to use both the Ultralytics CLI and the Python interface
     ```python
     from ultralytics import YOLO
 
-    model = YOLO("yolov8n.pt")
+    model = YOLO("yolo11n.pt")
     model.train(data="coco8.yaml", epochs=3)
     ```
 
 Refer to the quickstart guides for more detailed instructions [here](../quickstart.md#use-ultralytics-with-cli) and [here](../quickstart.md#use-ultralytics-with-python).
 
-### What is the advantage of using Ultralytics YOLOv8 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models?
+### What is the advantage of using Ultralytics YOLO11 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models?
 
-Ultralytics YOLOv8 offers several unique advantages over competing object detection models:
+Ultralytics YOLO11 offers several unique advantages over competing object detection models:
 
 - **Speed**: Faster inference and training times compared to models like Faster R-CNN and SSD.
 - **[Accuracy](https://www.ultralytics.com/glossary/accuracy)**: High accuracy in detection tasks with features like anchor-free design and enhanced augmentation strategies.
 - **Ease of Use**: Intuitive API and CLI for quick setup, making it accessible both to beginners and experts.
 
-To explore more about YOLOv8's features, visit the [Ultralytics YOLO](https://www.ultralytics.com/yolo) page for detailed insights.
+To explore more about YOLO11's features, visit the [Ultralytics YOLO](https://www.ultralytics.com/yolo) page for detailed insights.

docs/en/guides/conda-quickstart.md

@@ -73,7 +73,7 @@ With Ultralytics installed, you can now start using its robust features for [obj
 ```python
 from ultralytics import YOLO
 
-model = YOLO("yolov8n.pt")  # initialize model
+model = YOLO("yolo11n.pt")  # initialize model
 results = model("path/to/image.jpg")  # perform inference
 results[0].show()  # display results for the first image
 ```

docs/en/guides/coral-edge-tpu-on-raspberry-pi.md

@@ -1,10 +1,10 @@
 ---
 comments: true
-description: Learn how to boost your Raspberry Pi's ML performance using Coral Edge TPU with Ultralytics YOLOv8. Follow our detailed setup and installation guide.
-keywords: Coral Edge TPU, Raspberry Pi, YOLOv8, Ultralytics, TensorFlow Lite, ML inference, machine learning, AI, installation guide, setup tutorial
+description: Learn how to boost your Raspberry Pi's ML performance using Coral Edge TPU with Ultralytics YOLO11. Follow our detailed setup and installation guide.
+keywords: Coral Edge TPU, Raspberry Pi, YOLO11, Ultralytics, TensorFlow Lite, ML inference, machine learning, AI, installation guide, setup tutorial
 ---
 
-# Coral Edge TPU on a Raspberry Pi with Ultralytics YOLOv8 🚀
+# Coral Edge TPU on a Raspberry Pi with Ultralytics YOLO11 🚀
 
 <p align="center">
   <img width="800" src="https://github.com/ultralytics/docs/releases/download/0/edge-tpu-usb-accelerator-and-pi.avif" alt="Raspberry Pi single board computer with USB Edge TPU accelerator">
@@ -152,9 +152,9 @@ Find comprehensive information on the [Predict](../modes/predict.md) page for fu
 
 ## FAQ
 
-### What is a Coral Edge TPU and how does it enhance Raspberry Pi's performance with Ultralytics YOLOv8?
+### What is a Coral Edge TPU and how does it enhance Raspberry Pi's performance with Ultralytics YOLO11?
 
-The Coral Edge TPU is a compact device designed to add an Edge TPU coprocessor to your system. This coprocessor enables low-power, high-performance [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) inference, particularly optimized for TensorFlow Lite models. When using a Raspberry Pi, the Edge TPU accelerates ML model inference, significantly boosting performance, especially for Ultralytics YOLOv8 models. You can read more about the Coral Edge TPU on their [home page](https://coral.ai/products/accelerator).
+The Coral Edge TPU is a compact device designed to add an Edge TPU coprocessor to your system. This coprocessor enables low-power, high-performance [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) inference, particularly optimized for TensorFlow Lite models. When using a Raspberry Pi, the Edge TPU accelerates ML model inference, significantly boosting performance, especially for Ultralytics YOLO11 models. You can read more about the Coral Edge TPU on their [home page](https://coral.ai/products/accelerator).
 
 ### How do I install the Coral Edge TPU runtime on a Raspberry Pi?
 
@@ -166,9 +166,9 @@ sudo dpkg -i path/to/package.deb
 
 Make sure to uninstall any previous Coral Edge TPU runtime versions by following the steps outlined in the [Installation Walkthrough](#installation-walkthrough) section.
 
-### Can I export my Ultralytics YOLOv8 model to be compatible with Coral Edge TPU?
+### Can I export my Ultralytics YOLO11 model to be compatible with Coral Edge TPU?
 
-Yes, you can export your Ultralytics YOLOv8 model to be compatible with the Coral Edge TPU. It is recommended to perform the export on Google Colab, an x86_64 Linux machine, or using the [Ultralytics Docker container](docker-quickstart.md). You can also use Ultralytics HUB for exporting. Here is how you can export your model using Python and CLI:
+Yes, you can export your Ultralytics YOLO11 model to be compatible with the Coral Edge TPU. It is recommended to perform the export on Google Colab, an x86_64 Linux machine, or using the [Ultralytics Docker container](docker-quickstart.md). You can also use Ultralytics HUB for exporting. Here is how you can export your model using Python and CLI:
 
 !!! note "Exporting the model"
 
@@ -208,9 +208,9 @@ pip install -U tflite-runtime
 
 For a specific wheel, such as TensorFlow 2.15.0 `tflite-runtime`, you can download it from [this link](https://github.com/feranick/TFlite-builds/releases) and install it using `pip`. Detailed instructions are available in the section on running the model [Running the Model](#running-the-model).
 
-### How do I run inference with an exported YOLOv8 model on a Raspberry Pi using the Coral Edge TPU?
+### How do I run inference with an exported YOLO11 model on a Raspberry Pi using the Coral Edge TPU?
 
-After exporting your YOLOv8 model to an Edge TPU-compatible format, you can run inference using the following code snippets:
+After exporting your YOLO11 model to an Edge TPU-compatible format, you can run inference using the following code snippets:
 
 !!! note "Running the model"
 

docs/en/guides/data-collection-and-annotation.md

@@ -136,12 +136,12 @@ Bouncing your ideas and queries off other [computer vision](https://www.ultralyt
 
 ### Where to Find Help and Support
 
-- **GitHub Issues:** Visit the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
+- **GitHub Issues:** Visit the YOLO11 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
 - **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Refer to the [official YOLOv8 documentation](./index.md) for thorough guides and valuable insights on numerous computer vision tasks and projects.
+- **Ultralytics YOLO11 Documentation:** Refer to the [official YOLO11 documentation](./index.md) for thorough guides and valuable insights on numerous computer vision tasks and projects.
 
 ## Conclusion
 
@@ -159,7 +159,7 @@ Ensuring high consistency and accuracy in data annotation involves establishing
 
 ### How many images do I need for training Ultralytics YOLO models?
 
-For effective [transfer learning](https://www.ultralytics.com/glossary/transfer-learning) and object detection with Ultralytics YOLO models, start with a minimum of a few hundred annotated objects per class. If training for just one class, begin with at least 100 annotated images and train for approximately 100 [epochs](https://www.ultralytics.com/glossary/epoch). More complex tasks might require thousands of images per class to achieve high reliability and performance. Quality annotations are crucial, so ensure your data collection and annotation processes are rigorous and aligned with your project's specific goals. Explore detailed training strategies in the [YOLOv8 training guide](../modes/train.md).
+For effective [transfer learning](https://www.ultralytics.com/glossary/transfer-learning) and object detection with Ultralytics YOLO models, start with a minimum of a few hundred annotated objects per class. If training for just one class, begin with at least 100 annotated images and train for approximately 100 [epochs](https://www.ultralytics.com/glossary/epoch). More complex tasks might require thousands of images per class to achieve high reliability and performance. Quality annotations are crucial, so ensure your data collection and annotation processes are rigorous and aligned with your project's specific goals. Explore detailed training strategies in the [YOLO11 training guide](../modes/train.md).
 
 ### What are some popular tools for data annotation?
 

docs/en/guides/deepstream-nvidia-jetson.md

@@ -1,10 +1,10 @@
 ---
 comments: true
-description: Learn how to deploy Ultralytics YOLOv8 on NVIDIA Jetson devices using TensorRT and DeepStream SDK. Explore performance benchmarks and maximize AI capabilities.
-keywords: Ultralytics, YOLOv8, NVIDIA Jetson, JetPack, AI deployment, embedded systems, deep learning, TensorRT, DeepStream SDK, computer vision
+description: Learn how to deploy Ultralytics YOLO11 on NVIDIA Jetson devices using TensorRT and DeepStream SDK. Explore performance benchmarks and maximize AI capabilities.
+keywords: Ultralytics, YOLO11, NVIDIA Jetson, JetPack, AI deployment, embedded systems, deep learning, TensorRT, DeepStream SDK, computer vision
 ---
 
-# Ultralytics YOLOv8 on NVIDIA Jetson using DeepStream SDK and TensorRT
+# Ultralytics YOLO11 on NVIDIA Jetson using DeepStream SDK and TensorRT
 
 <p align="center">
   <br>
@@ -14,10 +14,10 @@ keywords: Ultralytics, YOLOv8, NVIDIA Jetson, JetPack, AI deployment, embedded s
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to Run Multiple Streams with DeepStream SDK on Jetson Nano using Ultralytics YOLOv8
+  <strong>Watch:</strong> How to Run Multiple Streams with DeepStream SDK on Jetson Nano using Ultralytics YOLO11
 </p>
 
-This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLOv8 on [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) devices using DeepStream SDK and TensorRT. Here we use TensorRT to maximize the inference performance on the Jetson platform.
+This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLO11 on [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) devices using DeepStream SDK and TensorRT. Here we use TensorRT to maximize the inference performance on the Jetson platform.
 
 <img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/deepstream-nvidia-jetson.avif" alt="DeepStream on NVIDIA Jetson">
 
@@ -33,7 +33,7 @@ This comprehensive guide provides a detailed walkthrough for deploying Ultralyti
 
 Before you start to follow this guide:
 
-- Visit our documentation, [Quick Start Guide: NVIDIA Jetson with Ultralytics YOLOv8](nvidia-jetson.md) to set up your NVIDIA Jetson device with Ultralytics YOLOv8
+- Visit our documentation, [Quick Start Guide: NVIDIA Jetson with Ultralytics YOLO11](nvidia-jetson.md) to set up your NVIDIA Jetson device with Ultralytics YOLO11
 - Install [DeepStream SDK](https://developer.nvidia.com/deepstream-getting-started) according to the JetPack version
 
     - For JetPack 4.6.4, install [DeepStream 6.0.1](https://docs.nvidia.com/metropolis/deepstream/6.0.1/dev-guide/text/DS_Quickstart.html)
@@ -43,7 +43,7 @@ Before you start to follow this guide:
 
     In this guide we have used the Debian package method of installing DeepStream SDK to the Jetson device. You can also visit the [DeepStream SDK on Jetson (Archived)](https://developer.nvidia.com/embedded/deepstream-on-jetson-downloads-archived) to access legacy versions of DeepStream.
 
-## DeepStream Configuration for YOLOv8
+## DeepStream Configuration for YOLO11
 
 Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) GitHub repository which includes NVIDIA DeepStream SDK support for YOLO models. We appreciate the efforts of marcoslucianops for his contributions!
 
@@ -61,7 +61,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
     cd DeepStream-Yolo
     ```
 
-3.  Download Ultralytics YOLOv8 detection model (.pt) of your choice from [YOLOv8 releases](https://github.com/ultralytics/assets/releases). Here we use [yolov8s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt).
+3.  Download Ultralytics YOLO11 detection model (.pt) of your choice from [YOLO11 releases](https://github.com/ultralytics/assets/releases). Here we use [yolov8s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt).
 
     ```bash
     wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt
@@ -69,7 +69,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
 
     !!! note
 
-        You can also use a [custom trained YOLOv8 model](https://docs.ultralytics.com/modes/train/).
+        You can also use a [custom trained YOLO11 model](https://docs.ultralytics.com/modes/train/).
 
 4.  Convert model to ONNX
 
@@ -179,7 +179,7 @@ deepstream-app -c deepstream_app_config.txt
 
     It will take a long time to generate the TensorRT engine file before starting the inference. So please be patient.
 
-<div align=center><img width=1000 src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-deepstream.avif" alt="YOLOv8 with deepstream"></div>
+<div align=center><img width=1000 src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-deepstream.avif" alt="YOLO11 with deepstream"></div>
 
 !!! tip
 
@@ -317,21 +317,21 @@ This guide was initially created by our friends at Seeed Studio, Lakshantha and
 
 ## FAQ
 
-### How do I set up Ultralytics YOLOv8 on an NVIDIA Jetson device?
+### How do I set up Ultralytics YOLO11 on an NVIDIA Jetson device?
 
-To set up Ultralytics YOLOv8 on an [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) device, you first need to install the [DeepStream SDK](https://developer.nvidia.com/deepstream-getting-started) compatible with your JetPack version. Follow the step-by-step guide in our [Quick Start Guide](nvidia-jetson.md) to configure your NVIDIA Jetson for YOLOv8 deployment.
+To set up Ultralytics YOLO11 on an [NVIDIA Jetson](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) device, you first need to install the [DeepStream SDK](https://developer.nvidia.com/deepstream-getting-started) compatible with your JetPack version. Follow the step-by-step guide in our [Quick Start Guide](nvidia-jetson.md) to configure your NVIDIA Jetson for YOLO11 deployment.
 
-### What is the benefit of using TensorRT with YOLOv8 on NVIDIA Jetson?
+### What is the benefit of using TensorRT with YOLO11 on NVIDIA Jetson?
 
-Using TensorRT with YOLOv8 optimizes the model for inference, significantly reducing latency and improving throughput on NVIDIA Jetson devices. TensorRT provides high-performance, low-latency [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference through layer fusion, precision calibration, and kernel auto-tuning. This leads to faster and more efficient execution, particularly useful for real-time applications like video analytics and autonomous machines.
+Using TensorRT with YOLO11 optimizes the model for inference, significantly reducing latency and improving throughput on NVIDIA Jetson devices. TensorRT provides high-performance, low-latency [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference through layer fusion, precision calibration, and kernel auto-tuning. This leads to faster and more efficient execution, particularly useful for real-time applications like video analytics and autonomous machines.
 
-### Can I run Ultralytics YOLOv8 with DeepStream SDK across different NVIDIA Jetson hardware?
+### Can I run Ultralytics YOLO11 with DeepStream SDK across different NVIDIA Jetson hardware?
 
-Yes, the guide for deploying Ultralytics YOLOv8 with the DeepStream SDK and TensorRT is compatible across the entire NVIDIA Jetson lineup. This includes devices like the Jetson Orin NX 16GB with [JetPack 5.1.3](https://developer.nvidia.com/embedded/jetpack-sdk-513) and the Jetson Nano 4GB with [JetPack 4.6.4](https://developer.nvidia.com/jetpack-sdk-464). Refer to the section [DeepStream Configuration for YOLOv8](#deepstream-configuration-for-yolov8) for detailed steps.
+Yes, the guide for deploying Ultralytics YOLO11 with the DeepStream SDK and TensorRT is compatible across the entire NVIDIA Jetson lineup. This includes devices like the Jetson Orin NX 16GB with [JetPack 5.1.3](https://developer.nvidia.com/embedded/jetpack-sdk-513) and the Jetson Nano 4GB with [JetPack 4.6.4](https://developer.nvidia.com/jetpack-sdk-464). Refer to the section [DeepStream Configuration for YOLO11](#deepstream-configuration-for-yolo11) for detailed steps.
 
-### How can I convert a YOLOv8 model to ONNX for DeepStream?
+### How can I convert a YOLO11 model to ONNX for DeepStream?
 
-To convert a YOLOv8 model to ONNX format for deployment with DeepStream, use the `utils/export_yoloV8.py` script from the [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) repository.
+To convert a YOLO11 model to ONNX format for deployment with DeepStream, use the `utils/export_yoloV8.py` script from the [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) repository.
 
 Here's an example command:
 
@@ -341,12 +341,12 @@ python3 utils/export_yoloV8.py -w yolov8s.pt --opset 12 --simplify
 
 For more details on model conversion, check out our [model export section](../modes/export.md).
 
-### What are the performance benchmarks for YOLOv8 on NVIDIA Jetson Orin NX?
+### What are the performance benchmarks for YOLO on NVIDIA Jetson Orin NX?
 
-The performance of YOLOv8 models on NVIDIA Jetson Orin NX 16GB varies based on TensorRT precision levels. For example, YOLOv8s models achieve:
+The performance of YOLO11 models on NVIDIA Jetson Orin NX 16GB varies based on TensorRT precision levels. For example, YOLOv8s models achieve:
 
 - **FP32 Precision**: 15.63 ms/im, 64 FPS
 - **FP16 Precision**: 7.94 ms/im, 126 FPS
 - **INT8 Precision**: 5.53 ms/im, 181 FPS
 
-These benchmarks underscore the efficiency and capability of using TensorRT-optimized YOLOv8 models on NVIDIA Jetson hardware. For further details, see our [Benchmark Results](#benchmark-results) section.
+These benchmarks underscore the efficiency and capability of using TensorRT-optimized YOLO11 models on NVIDIA Jetson hardware. For further details, see our [Benchmark Results](#benchmark-results) section.

docs/en/guides/defining-project-goals.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Learn how to define clear goals and objectives for your computer vision project with our practical guide. Includes tips on problem statements, measurable objectives, and key decisions.
-keywords: computer vision, project planning, problem statement, measurable objectives, dataset preparation, model selection, YOLOv8, Ultralytics
+keywords: computer vision, project planning, problem statement, measurable objectives, dataset preparation, model selection, YOLO11, Ultralytics
 ---
 
 # A Practical Guide for Defining Your [Computer Vision](https://www.ultralytics.com/glossary/computer-vision-cv) Project
@@ -30,7 +30,7 @@ Let's walk through an example.
 Consider a computer vision project where you want to [estimate the speed of vehicles](./speed-estimation.md) on a highway. The core issue is that current speed monitoring methods are inefficient and error-prone due to outdated radar systems and manual processes. The project aims to develop a real-time computer vision system that can replace legacy [speed estimation](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) systems.
 
 <p align="center">
-  <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/speed-estimation-using-yolov8.avif" alt="Speed Estimation Using YOLOv8">
+  <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/speed-estimation-using-yolov8.avif" alt="Speed Estimation Using YOLO11">
 </p>
 
 Primary users include traffic management authorities and law enforcement, while secondary stakeholders are highway planners and the public benefiting from safer roads. Key requirements involve evaluating budget, time, and personnel, as well as addressing technical needs like high-resolution cameras and real-time data processing. Additionally, regulatory constraints on privacy and [data security](https://www.ultralytics.com/glossary/data-security) must be considered.
@@ -85,7 +85,7 @@ The most popular computer vision tasks include [image classification](https://ww
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/image-classification-vs-object-detection-vs-image-segmentation.avif" alt="Overview of Computer Vision Tasks">
 </p>
 
-For a detailed explanation of various tasks, please take a look at the Ultralytics Docs page on [YOLOv8 Tasks](../tasks/index.md).
+For a detailed explanation of various tasks, please take a look at the Ultralytics Docs page on [YOLO11 Tasks](../tasks/index.md).
 
 ### Can a Pre-trained Model Remember Classes It Knew Before Custom Training?
 
@@ -114,12 +114,12 @@ Connecting with other computer vision enthusiasts can be incredibly helpful for
 
 ### Community Support Channels
 
-- **GitHub Issues:** Head over to the YOLOv8 GitHub repository. You can use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers can assist with specific problems you encounter.
+- **GitHub Issues:** Head over to the YOLO11 GitHub repository. You can use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers can assist with specific problems you encounter.
 - **Ultralytics Discord Server:** Become part of the [Ultralytics Discord server](https://discord.com/invite/ultralytics). Connect with fellow users and developers, seek support, exchange knowledge, and discuss ideas.
 
 ### Comprehensive Guides and Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Explore the [official YOLOv8 documentation](./index.md) for in-depth guides and valuable tips on various computer vision tasks and projects.
+- **Ultralytics YOLO11 Documentation:** Explore the [official YOLO11 documentation](./index.md) for in-depth guides and valuable tips on various computer vision tasks and projects.
 
 ## Conclusion
 
@@ -138,11 +138,11 @@ To define a clear problem statement for your Ultralytics computer vision project
 
 Providing a well-defined problem statement ensures that the project remains focused and aligned with your objectives. For a detailed guide, refer to our [practical guide](#defining-a-clear-problem-statement).
 
-### Why should I use Ultralytics YOLOv8 for speed estimation in my computer vision project?
+### Why should I use Ultralytics YOLO11 for speed estimation in my computer vision project?
 
-Ultralytics YOLOv8 is ideal for speed estimation because of its real-time object tracking capabilities, high accuracy, and robust performance in detecting and monitoring vehicle speeds. It overcomes inefficiencies and inaccuracies of traditional radar systems by leveraging cutting-edge computer vision technology. Check out our blog on [speed estimation using YOLOv8](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) for more insights and practical examples.
+Ultralytics YOLO11 is ideal for speed estimation because of its real-time object tracking capabilities, high accuracy, and robust performance in detecting and monitoring vehicle speeds. It overcomes inefficiencies and inaccuracies of traditional radar systems by leveraging cutting-edge computer vision technology. Check out our blog on [speed estimation using YOLO11](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) for more insights and practical examples.
 
-### How do I set effective measurable objectives for my computer vision project with Ultralytics YOLOv8?
+### How do I set effective measurable objectives for my computer vision project with Ultralytics YOLO11?
 
 Set effective and measurable objectives using the SMART criteria:
 

docs/en/guides/distance-calculation.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn how to calculate distances between objects using Ultralytics YOLOv8 for accurate spatial positioning and scene understanding.
-keywords: Ultralytics, YOLOv8, distance calculation, computer vision, object tracking, spatial positioning
+description: Learn how to calculate distances between objects using Ultralytics YOLO11 for accurate spatial positioning and scene understanding.
+keywords: Ultralytics, YOLO11, distance calculation, computer vision, object tracking, spatial positioning
 ---
 
-# Distance Calculation using Ultralytics YOLOv8
+# Distance Calculation using Ultralytics YOLO11
 
 ## What is Distance Calculation?
 
-Measuring the gap between two objects is known as distance calculation within a specified space. In the case of [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), the [bounding box](https://www.ultralytics.com/glossary/bounding-box) centroid is employed to calculate the distance for bounding boxes highlighted by the user.
+Measuring the gap between two objects is known as distance calculation within a specified space. In the case of [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics), the [bounding box](https://www.ultralytics.com/glossary/bounding-box) centroid is employed to calculate the distance for bounding boxes highlighted by the user.
 
 <p align="center">
   <br>
@@ -18,14 +18,14 @@ Measuring the gap between two objects is known as distance calculation within a
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Distance Calculation using Ultralytics YOLOv8
+  <strong>Watch:</strong> Distance Calculation using Ultralytics YOLO11
 </p>
 
 ## Visuals
 
-|                                         Distance Calculation using Ultralytics YOLOv8                                         |
+|                                         Distance Calculation using Ultralytics YOLO11                                         |
 | :---------------------------------------------------------------------------------------------------------------------------: |
-| ![Ultralytics YOLOv8 Distance Calculation](https://github.com/ultralytics/docs/releases/download/0/distance-calculation.avif) |
+| ![Ultralytics YOLO11 Distance Calculation](https://github.com/ultralytics/docs/releases/download/0/distance-calculation.avif) |
 
 ## Advantages of Distance Calculation?
 
@@ -36,7 +36,7 @@ Measuring the gap between two objects is known as distance calculation within a
 
     - Click on any two bounding boxes with Left Mouse click for distance calculation
 
-!!! example "Distance Calculation using YOLOv8 Example"
+!!! example "Distance Calculation using YOLO11 Example"
 
     === "Video Stream"
 
@@ -45,7 +45,7 @@ Measuring the gap between two objects is known as distance calculation within a
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         names = model.model.names
 
         cap = cv2.VideoCapture("path/to/video/file.mp4")
@@ -98,29 +98,29 @@ Measuring the gap between two objects is known as distance calculation within a
 
 ## FAQ
 
-### How do I calculate distances between objects using Ultralytics YOLOv8?
+### How do I calculate distances between objects using Ultralytics YOLO11?
 
-To calculate distances between objects using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), you need to identify the bounding box centroids of the detected objects. This process involves initializing the `DistanceCalculation` class from Ultralytics' `solutions` module and using the model's tracking outputs to calculate the distances. You can refer to the implementation in the [distance calculation example](#distance-calculation-using-ultralytics-yolov8).
+To calculate distances between objects using [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics), you need to identify the bounding box centroids of the detected objects. This process involves initializing the `DistanceCalculation` class from Ultralytics' `solutions` module and using the model's tracking outputs to calculate the distances. You can refer to the implementation in the [distance calculation example](#distance-calculation-using-ultralytics-yolo11).
 
-### What are the advantages of using distance calculation with Ultralytics YOLOv8?
+### What are the advantages of using distance calculation with Ultralytics YOLO11?
 
-Using distance calculation with Ultralytics YOLOv8 offers several advantages:
+Using distance calculation with Ultralytics YOLO11 offers several advantages:
 
 - **Localization Precision:** Provides accurate spatial positioning for objects.
 - **Size Estimation:** Helps estimate physical sizes, contributing to better contextual understanding.
 - **Scene Understanding:** Enhances 3D scene comprehension, aiding improved decision-making in applications like autonomous driving and surveillance.
 
-### Can I perform distance calculation in real-time video streams with Ultralytics YOLOv8?
+### Can I perform distance calculation in real-time video streams with Ultralytics YOLO11?
 
-Yes, you can perform distance calculation in real-time video streams with Ultralytics YOLOv8. The process involves capturing video frames using [OpenCV](https://www.ultralytics.com/glossary/opencv), running YOLOv8 [object detection](https://www.ultralytics.com/glossary/object-detection), and using the `DistanceCalculation` class to calculate distances between objects in successive frames. For a detailed implementation, see the [video stream example](#distance-calculation-using-ultralytics-yolov8).
+Yes, you can perform distance calculation in real-time video streams with Ultralytics YOLO11. The process involves capturing video frames using [OpenCV](https://www.ultralytics.com/glossary/opencv), running YOLO11 [object detection](https://www.ultralytics.com/glossary/object-detection), and using the `DistanceCalculation` class to calculate distances between objects in successive frames. For a detailed implementation, see the [video stream example](#distance-calculation-using-ultralytics-yolo11).
 
-### How do I delete points drawn during distance calculation using Ultralytics YOLOv8?
+### How do I delete points drawn during distance calculation using Ultralytics YOLO11?
 
-To delete points drawn during distance calculation with Ultralytics YOLOv8, you can use a right mouse click. This action will clear all the points you have drawn. For more details, refer to the note section under the [distance calculation example](#distance-calculation-using-ultralytics-yolov8).
+To delete points drawn during distance calculation with Ultralytics YOLO11, you can use a right mouse click. This action will clear all the points you have drawn. For more details, refer to the note section under the [distance calculation example](#distance-calculation-using-ultralytics-yolo11).
 
-### What are the key arguments for initializing the DistanceCalculation class in Ultralytics YOLOv8?
+### What are the key arguments for initializing the DistanceCalculation class in Ultralytics YOLO11?
 
-The key arguments for initializing the `DistanceCalculation` class in Ultralytics YOLOv8 include:
+The key arguments for initializing the `DistanceCalculation` class in Ultralytics YOLO11 include:
 
 - `names`: Dictionary mapping class indices to class names.
 - `view_img`: Flag to indicate if the video stream should be displayed.

docs/en/guides/docker-quickstart.md

@@ -197,10 +197,10 @@ Setup and configuration of an X11 or Wayland display server is outside the scope
 
 ### Using Docker with a GUI
 
-Now you can display graphical applications inside your Docker container. For example, you can run the following [CLI command](../usage/cli.md) to visualize the [predictions](../modes/predict.md) from a [YOLOv8 model](../models/yolov8.md):
+Now you can display graphical applications inside your Docker container. For example, you can run the following [CLI command](../usage/cli.md) to visualize the [predictions](../modes/predict.md) from a [YOLO11 model](../models/yolo11.md):
 
 ```bash
-yolo predict model=yolov8n.pt show=True
+yolo predict model=yolo11n.pt show=True
 ```
 
 ??? info "Testing"

docs/en/guides/heatmaps.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Transform complex data into insightful heatmaps using Ultralytics YOLOv8. Discover patterns, trends, and anomalies with vibrant visualizations.
-keywords: Ultralytics, YOLOv8, heatmaps, data visualization, data analysis, complex data, patterns, trends, anomalies
+description: Transform complex data into insightful heatmaps using Ultralytics YOLO11. Discover patterns, trends, and anomalies with vibrant visualizations.
+keywords: Ultralytics, YOLO11, heatmaps, data visualization, data analysis, complex data, patterns, trends, anomalies
 ---
 
-# Advanced [Data Visualization](https://www.ultralytics.com/glossary/data-visualization): Heatmaps using Ultralytics YOLOv8 🚀
+# Advanced [Data Visualization](https://www.ultralytics.com/glossary/data-visualization): Heatmaps using Ultralytics YOLO11 🚀
 
 ## Introduction to Heatmaps
 
-A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.
+A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.
 
 <p align="center">
   <br>
@@ -18,7 +18,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Heatmaps using Ultralytics YOLOv8
+  <strong>Watch:</strong> Heatmaps using Ultralytics YOLO11
 </p>
 
 ## Why Choose Heatmaps for Data Analysis?
@@ -31,15 +31,15 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
 |                                                                    Transportation                                                                    |                                                                Retail                                                                |
 | :--------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: |
-| ![Ultralytics YOLOv8 Transportation Heatmap](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-transportation-heatmap.avif) | ![Ultralytics YOLOv8 Retail Heatmap](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-retail-heatmap.avif) |
-|                                                      Ultralytics YOLOv8 Transportation Heatmap                                                       |                                                  Ultralytics YOLOv8 Retail Heatmap                                                   |
+| ![Ultralytics YOLO11 Transportation Heatmap](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-transportation-heatmap.avif) | ![Ultralytics YOLO11 Retail Heatmap](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-retail-heatmap.avif) |
+|                                                      Ultralytics YOLO11 Transportation Heatmap                                                       |                                                  Ultralytics YOLO11 Retail Heatmap                                                   |
 
 !!! tip "Heatmap Configuration"
 
     - `heatmap_alpha`: Ensure this value is within the range (0.0 - 1.0).
     - `decay_factor`: Used for removing heatmap after an object is no longer in the frame, its value should also be in the range (0.0 - 1.0).
 
-!!! example "Heatmaps using Ultralytics YOLOv8 Example"
+!!! example "Heatmaps using Ultralytics YOLO11 Example"
 
     === "Heatmap"
 
@@ -48,7 +48,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -86,7 +86,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -127,7 +127,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -169,7 +169,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -211,7 +211,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8s.pt")  # YOLOv8 custom/pretrained model
+        model = YOLO("yolo11n.pt")  # YOLO11 custom/pretrained model
 
         im0 = cv2.imread("path/to/image.png")  # path to image file
         h, w = im0.shape[:2]  # image height and width
@@ -236,7 +236,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -326,20 +326,20 @@ These colormaps are commonly used for visualizing data with different color repr
 
 ## FAQ
 
-### How does Ultralytics YOLOv8 generate heatmaps and what are their benefits?
+### How does Ultralytics YOLO11 generate heatmaps and what are their benefits?
 
-Ultralytics YOLOv8 generates heatmaps by transforming complex data into a color-coded matrix where different hues represent data intensities. Heatmaps make it easier to visualize patterns, correlations, and anomalies in the data. Warmer hues indicate higher values, while cooler tones represent lower values. The primary benefits include intuitive visualization of data distribution, efficient pattern detection, and enhanced spatial analysis for decision-making. For more details and configuration options, refer to the [Heatmap Configuration](#arguments-heatmap) section.
+Ultralytics YOLO11 generates heatmaps by transforming complex data into a color-coded matrix where different hues represent data intensities. Heatmaps make it easier to visualize patterns, correlations, and anomalies in the data. Warmer hues indicate higher values, while cooler tones represent lower values. The primary benefits include intuitive visualization of data distribution, efficient pattern detection, and enhanced spatial analysis for decision-making. For more details and configuration options, refer to the [Heatmap Configuration](#arguments-heatmap) section.
 
-### Can I use Ultralytics YOLOv8 to perform object tracking and generate a heatmap simultaneously?
+### Can I use Ultralytics YOLO11 to perform object tracking and generate a heatmap simultaneously?
 
-Yes, Ultralytics YOLOv8 supports object tracking and heatmap generation concurrently. This can be achieved through its `Heatmap` solution integrated with object tracking models. To do so, you need to initialize the heatmap object and use YOLOv8's tracking capabilities. Here's a simple example:
+Yes, Ultralytics YOLO11 supports object tracking and heatmap generation concurrently. This can be achieved through its `Heatmap` solution integrated with object tracking models. To do so, you need to initialize the heatmap object and use YOLO11's tracking capabilities. Here's a simple example:
 
 ```python
 import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("path/to/video/file.mp4")
 heatmap_obj = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", names=model.names)
 
@@ -359,11 +359,11 @@ cv2.destroyAllWindows()
 
 For further guidance, check the [Tracking Mode](../modes/track.md) page.
 
-### What makes Ultralytics YOLOv8 heatmaps different from other data visualization tools like those from [OpenCV](https://www.ultralytics.com/glossary/opencv) or Matplotlib?
+### What makes Ultralytics YOLO11 heatmaps different from other data visualization tools like those from [OpenCV](https://www.ultralytics.com/glossary/opencv) or Matplotlib?
 
-Ultralytics YOLOv8 heatmaps are specifically designed for integration with its [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking models, providing an end-to-end solution for real-time data analysis. Unlike generic visualization tools like OpenCV or Matplotlib, YOLOv8 heatmaps are optimized for performance and automated processing, supporting features like persistent tracking, decay factor adjustment, and real-time video overlay. For more information on YOLOv8's unique features, visit the [Ultralytics YOLOv8 Introduction](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
+Ultralytics YOLO11 heatmaps are specifically designed for integration with its [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking models, providing an end-to-end solution for real-time data analysis. Unlike generic visualization tools like OpenCV or Matplotlib, YOLO11 heatmaps are optimized for performance and automated processing, supporting features like persistent tracking, decay factor adjustment, and real-time video overlay. For more information on YOLO11's unique features, visit the [Ultralytics YOLO11 Introduction](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
 
-### How can I visualize only specific object classes in heatmaps using Ultralytics YOLOv8?
+### How can I visualize only specific object classes in heatmaps using Ultralytics YOLO11?
 
 You can visualize specific object classes by specifying the desired classes in the `track()` method of the YOLO model. For instance, if you only want to visualize cars and persons (assuming their class indices are 0 and 2), you can set the `classes` parameter accordingly.
 
@@ -372,7 +372,7 @@ import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("path/to/video/file.mp4")
 heatmap_obj = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", names=model.names)
 
@@ -391,6 +391,6 @@ cap.release()
 cv2.destroyAllWindows()
 ```
 
-### Why should businesses choose Ultralytics YOLOv8 for heatmap generation in data analysis?
+### Why should businesses choose Ultralytics YOLO11 for heatmap generation in data analysis?
 
-Ultralytics YOLOv8 offers seamless integration of advanced object detection and real-time heatmap generation, making it an ideal choice for businesses looking to visualize data more effectively. The key advantages include intuitive data distribution visualization, efficient pattern detection, and enhanced spatial analysis for better decision-making. Additionally, YOLOv8's cutting-edge features such as persistent tracking, customizable colormaps, and support for various export formats make it superior to other tools like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and OpenCV for comprehensive data analysis. Learn more about business applications at [Ultralytics Plans](https://www.ultralytics.com/plans).
+Ultralytics YOLO11 offers seamless integration of advanced object detection and real-time heatmap generation, making it an ideal choice for businesses looking to visualize data more effectively. The key advantages include intuitive data distribution visualization, efficient pattern detection, and enhanced spatial analysis for better decision-making. Additionally, YOLO11's cutting-edge features such as persistent tracking, customizable colormaps, and support for various export formats make it superior to other tools like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and OpenCV for comprehensive data analysis. Learn more about business applications at [Ultralytics Plans](https://www.ultralytics.com/plans).

docs/en/guides/hyperparameter-tuning.md

@@ -23,7 +23,7 @@ Hyperparameters are high-level, structural settings for the algorithm. They are
   <img width="640" src="https://github.com/ultralytics/docs/releases/download/0/hyperparameter-tuning-visual.avif" alt="Hyperparameter Tuning Visual">
 </p>
 
-For a full list of augmentation hyperparameters used in YOLOv8 please refer to the [configurations page](../usage/cfg.md#augmentation-settings).
+For a full list of augmentation hyperparameters used in YOLO11 please refer to the [configurations page](../usage/cfg.md#augmentation-settings).
 
 ### Genetic Evolution and Mutation
 
@@ -67,7 +67,7 @@ The process is repeated until either the set number of iterations is reached or
 
 ## Usage Example
 
-Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning of YOLOv8n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.
+Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning of YOLO11n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.
 
 !!! example
 
@@ -77,7 +77,7 @@ Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyp
         from ultralytics import YOLO
 
         # Initialize the YOLO model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Tune hyperparameters on COCO8 for 30 epochs
         model.tune(data="coco8.yaml", epochs=30, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
@@ -202,7 +202,7 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
 
 1. [Hyperparameter Optimization in Wikipedia](https://en.wikipedia.org/wiki/Hyperparameter_optimization)
 2. [YOLOv5 Hyperparameter Evolution Guide](../yolov5/tutorials/hyperparameter_evolution.md)
-3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](../integrations/ray-tune.md)
+3. [Efficient Hyperparameter Tuning with Ray Tune and YOLO11](../integrations/ray-tune.md)
 
 For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://discord.com/invite/ultralytics).
 
@@ -220,7 +220,7 @@ To optimize the learning rate for Ultralytics YOLO, start by setting an initial
         from ultralytics import YOLO
 
         # Initialize the YOLO model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Tune hyperparameters on COCO8 for 30 epochs
         model.tune(data="coco8.yaml", epochs=30, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
@@ -228,9 +228,9 @@ To optimize the learning rate for Ultralytics YOLO, start by setting an initial
 
 For more details, check the [Ultralytics YOLO configuration page](../usage/cfg.md#augmentation-settings).
 
-### What are the benefits of using genetic algorithms for hyperparameter tuning in YOLOv8?
+### What are the benefits of using genetic algorithms for hyperparameter tuning in YOLO11?
 
-Genetic algorithms in Ultralytics YOLOv8 provide a robust method for exploring the hyperparameter space, leading to highly optimized model performance. Key benefits include:
+Genetic algorithms in Ultralytics YOLO11 provide a robust method for exploring the hyperparameter space, leading to highly optimized model performance. Key benefits include:
 
 - **Efficient Search**: Genetic algorithms like mutation can quickly explore a large set of hyperparameters.
 - **Avoiding Local Minima**: By introducing randomness, they help in avoiding local minima, ensuring better global optimization.
@@ -240,7 +240,7 @@ To see how genetic algorithms can optimize hyperparameters, check out the [hyper
 
 ### How long does the hyperparameter tuning process take for Ultralytics YOLO?
 
-The time required for hyperparameter tuning with Ultralytics YOLO largely depends on several factors such as the size of the dataset, the complexity of the model architecture, the number of iterations, and the computational resources available. For instance, tuning YOLOv8n on a dataset like COCO8 for 30 epochs might take several hours to days, depending on the hardware.
+The time required for hyperparameter tuning with Ultralytics YOLO largely depends on several factors such as the size of the dataset, the complexity of the model architecture, the number of iterations, and the computational resources available. For instance, tuning YOLO11n on a dataset like COCO8 for 30 epochs might take several hours to days, depending on the hardware.
 
 To effectively manage tuning time, define a clear tuning budget beforehand ([internal section link](#preparing-for-hyperparameter-tuning)). This helps in balancing resource allocation and optimization goals.
 

docs/en/guides/index.md

@@ -18,7 +18,7 @@ Whether you're a beginner or an expert in [deep learning](https://www.ultralytic
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Ultralytics YOLOv8 Guides Overview
+  <strong>Watch:</strong> Ultralytics YOLO11 Guides Overview
 </p>
 
 ## Guides
@@ -30,14 +30,14 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Model Deployment Options](model-deployment-options.md): Overview of YOLO [model deployment](https://www.ultralytics.com/glossary/model-deployment) formats like ONNX, OpenVINO, and TensorRT, with pros and cons for each to inform your deployment strategy.
 - [K-Fold Cross Validation](kfold-cross-validation.md) 🚀 NEW: Learn how to improve model generalization using K-Fold cross-validation technique.
 - [Hyperparameter Tuning](hyperparameter-tuning.md) 🚀 NEW: Discover how to optimize your YOLO models by fine-tuning hyperparameters using the Tuner class and genetic evolution algorithms.
-- [SAHI Tiled Inference](sahi-tiled-inference.md) 🚀 NEW: Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLOv8 for object detection in high-resolution images.
+- [SAHI Tiled Inference](sahi-tiled-inference.md) 🚀 NEW: Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLO11 for object detection in high-resolution images.
 - [AzureML Quickstart](azureml-quickstart.md) 🚀 NEW: Get up and running with Ultralytics YOLO models on Microsoft's Azure [Machine Learning](https://www.ultralytics.com/glossary/machine-learning-ml) platform. Learn how to train, deploy, and scale your object detection projects in the cloud.
 - [Conda Quickstart](conda-quickstart.md) 🚀 NEW: Step-by-step guide to setting up a [Conda](https://anaconda.org/conda-forge/ultralytics) environment for Ultralytics. Learn how to install and start using the Ultralytics package efficiently with Conda.
 - [Docker Quickstart](docker-quickstart.md) 🚀 NEW: Complete guide to setting up and using Ultralytics YOLO models with [Docker](https://hub.docker.com/r/ultralytics/ultralytics). Learn how to install Docker, manage GPU support, and run YOLO models in isolated containers for consistent development and deployment.
 - [Raspberry Pi](raspberry-pi.md) 🚀 NEW: Quickstart tutorial to run YOLO models to the latest Raspberry Pi hardware.
 - [NVIDIA Jetson](nvidia-jetson.md) 🚀 NEW: Quickstart guide for deploying YOLO models on NVIDIA Jetson devices.
 - [DeepStream on NVIDIA Jetson](deepstream-nvidia-jetson.md) 🚀 NEW: Quickstart guide for deploying YOLO models on NVIDIA Jetson devices using DeepStream and TensorRT.
-- [Triton Inference Server Integration](triton-inference-server.md) 🚀 NEW: Dive into the integration of Ultralytics YOLOv8 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments.
+- [Triton Inference Server Integration](triton-inference-server.md) 🚀 NEW: Dive into the integration of Ultralytics YOLO11 with NVIDIA's Triton Inference Server for scalable and efficient deep learning inference deployments.
 - [YOLO Thread-Safe Inference](yolo-thread-safe-inference.md) 🚀 NEW: Guidelines for performing inference with YOLO models in a thread-safe manner. Learn the importance of thread safety and best practices to prevent race conditions and ensure consistent predictions.
 - [Isolating Segmentation Objects](isolating-segmentation-objects.md) 🚀 NEW: Step-by-step recipe and explanation on how to extract and/or isolate objects from images using Ultralytics Segmentation.
 - [Edge TPU on Raspberry Pi](coral-edge-tpu-on-raspberry-pi.md): [Google Edge TPU](https://coral.ai/products/accelerator) accelerates YOLO inference on [Raspberry Pi](https://www.raspberrypi.com/).
@@ -46,7 +46,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Steps of a Computer Vision Project ](steps-of-a-cv-project.md) 🚀 NEW: Learn about the key steps involved in a computer vision project, including defining goals, selecting models, preparing data, and evaluating results.
 - [Defining A Computer Vision Project's Goals](defining-project-goals.md) 🚀 NEW: Walk through how to effectively define clear and measurable goals for your computer vision project. Learn the importance of a well-defined problem statement and how it creates a roadmap for your project.
 - [Data Collection and Annotation](data-collection-and-annotation.md) 🚀 NEW: Explore the tools, techniques, and best practices for collecting and annotating data to create high-quality inputs for your computer vision models.
-- [Preprocessing Annotated Data](preprocessing_annotated_data.md) 🚀 NEW: Learn about preprocessing and augmenting image data in computer vision projects using YOLOv8, including normalization, dataset augmentation, splitting, and exploratory data analysis (EDA).
+- [Preprocessing Annotated Data](preprocessing_annotated_data.md) 🚀 NEW: Learn about preprocessing and augmenting image data in computer vision projects using YOLO11, including normalization, dataset augmentation, splitting, and exploratory data analysis (EDA).
 - [Tips for Model Training](model-training-tips.md) 🚀 NEW: Explore tips on optimizing [batch sizes](https://www.ultralytics.com/glossary/batch-size), using [mixed precision](https://www.ultralytics.com/glossary/mixed-precision), applying pre-trained weights, and more to make training your computer vision model a breeze.
 - [Insights on Model Evaluation and Fine-Tuning](model-evaluation-insights.md) 🚀 NEW: Gain insights into the strategies and best practices for evaluating and fine-tuning your computer vision models. Learn about the iterative process of refining models to achieve optimal results.
 - [A Guide on Model Testing](model-testing.md) 🚀 NEW: A thorough guide on testing your computer vision models in realistic settings. Learn how to verify accuracy, reliability, and performance in line with project goals.
@@ -75,14 +75,14 @@ Training a custom object detection model with Ultralytics YOLO is straightforwar
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8s.pt")  # Load a pre-trained YOLO model
+        model = YOLO("yolo11n.pt")  # Load a pre-trained YOLO model
         model.train(data="path/to/dataset.yaml", epochs=50)  # Train on custom dataset
         ```
 
     === "CLI"
 
         ```bash
-        yolo task=detect mode=train model=yolov8s.pt data=path/to/dataset.yaml epochs=50
+        yolo task=detect mode=train model=yolo11n.pt data=path/to/dataset.yaml epochs=50
         ```
 
 For detailed dataset formatting and additional options, refer to our [Tips for Model Training](model-training-tips.md) guide.

docs/en/guides/instance-segmentation-and-tracking.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Master instance segmentation and tracking with Ultralytics YOLOv8. Learn techniques for precise object identification and tracking.
-keywords: instance segmentation, tracking, YOLOv8, Ultralytics, object detection, machine learning, computer vision, python
+description: Master instance segmentation and tracking with Ultralytics YOLO11. Learn techniques for precise object identification and tracking.
+keywords: instance segmentation, tracking, YOLO11, Ultralytics, object detection, machine learning, computer vision, python
 ---
 
-# Instance Segmentation and Tracking using Ultralytics YOLOv8 🚀
+# Instance Segmentation and Tracking using Ultralytics YOLO11 🚀
 
 ## What is [Instance Segmentation](https://www.ultralytics.com/glossary/instance-segmentation)?
 
-[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike [semantic segmentation](https://www.ultralytics.com/glossary/semantic-segmentation), it uniquely labels and precisely delineates each object, crucial for tasks like [object detection](https://www.ultralytics.com/glossary/object-detection) and medical imaging.
+[Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike [semantic segmentation](https://www.ultralytics.com/glossary/semantic-segmentation), it uniquely labels and precisely delineates each object, crucial for tasks like [object detection](https://www.ultralytics.com/glossary/object-detection) and medical imaging.
 
 There are two types of instance segmentation tracking available in the Ultralytics package:
 
@@ -24,7 +24,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Instance Segmentation with Object Tracking using Ultralytics YOLOv8
+  <strong>Watch:</strong> Instance Segmentation with Object Tracking using Ultralytics YOLO11
 </p>
 
 ## Samples
@@ -44,7 +44,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 
-        model = YOLO("yolov8n-seg.pt")  # segmentation model
+        model = YOLO("yolo11n-seg.pt")  # segmentation model
         names = model.model.names
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -91,7 +91,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
 
         track_history = defaultdict(lambda: [])
 
-        model = YOLO("yolov8n-seg.pt")  # segmentation model
+        model = YOLO("yolo11n-seg.pt")  # segmentation model
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
 
@@ -142,9 +142,9 @@ For any inquiries, feel free to post your questions in the [Ultralytics Issue Se
 
 ## FAQ
 
-### How do I perform instance segmentation using Ultralytics YOLOv8?
+### How do I perform instance segmentation using Ultralytics YOLO11?
 
-To perform instance segmentation using Ultralytics YOLOv8, initialize the YOLO model with a segmentation version of YOLOv8 and process video frames through it. Here's a simplified code example:
+To perform instance segmentation using Ultralytics YOLO11, initialize the YOLO model with a segmentation version of YOLO11 and process video frames through it. Here's a simplified code example:
 
 !!! example
 
@@ -156,7 +156,7 @@ To perform instance segmentation using Ultralytics YOLOv8, initialize the YOLO m
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 
-        model = YOLO("yolov8n-seg.pt")  # segmentation model
+        model = YOLO("yolo11n-seg.pt")  # segmentation model
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
 
@@ -186,17 +186,17 @@ To perform instance segmentation using Ultralytics YOLOv8, initialize the YOLO m
         cv2.destroyAllWindows()
         ```
 
-Learn more about instance segmentation in the [Ultralytics YOLOv8 guide](#what-is-instance-segmentation).
+Learn more about instance segmentation in the [Ultralytics YOLO11 guide](#what-is-instance-segmentation).
 
-### What is the difference between instance segmentation and object tracking in Ultralytics YOLOv8?
+### What is the difference between instance segmentation and object tracking in Ultralytics YOLO11?
 
-Instance segmentation identifies and outlines individual objects within an image, giving each object a unique label and mask. Object tracking extends this by assigning consistent labels to objects across video frames, facilitating continuous tracking of the same objects over time. Learn more about the distinctions in the [Ultralytics YOLOv8 documentation](#samples).
+Instance segmentation identifies and outlines individual objects within an image, giving each object a unique label and mask. Object tracking extends this by assigning consistent labels to objects across video frames, facilitating continuous tracking of the same objects over time. Learn more about the distinctions in the [Ultralytics YOLO11 documentation](#samples).
 
-### Why should I use Ultralytics YOLOv8 for instance segmentation and tracking over other models like Mask R-CNN or Faster R-CNN?
+### Why should I use Ultralytics YOLO11 for instance segmentation and tracking over other models like Mask R-CNN or Faster R-CNN?
 
-Ultralytics YOLOv8 offers real-time performance, superior [accuracy](https://www.ultralytics.com/glossary/accuracy), and ease of use compared to other models like Mask R-CNN or Faster R-CNN. YOLOv8 provides a seamless integration with Ultralytics HUB, allowing users to manage models, datasets, and training pipelines efficiently. Discover more about the benefits of YOLOv8 in the [Ultralytics blog](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
+Ultralytics YOLO11 offers real-time performance, superior [accuracy](https://www.ultralytics.com/glossary/accuracy), and ease of use compared to other models like Mask R-CNN or Faster R-CNN. YOLO11 provides a seamless integration with Ultralytics HUB, allowing users to manage models, datasets, and training pipelines efficiently. Discover more about the benefits of YOLO11 in the [Ultralytics blog](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
 
-### How can I implement object tracking using Ultralytics YOLOv8?
+### How can I implement object tracking using Ultralytics YOLO11?
 
 To implement object tracking, use the `model.track` method and ensure that each object's ID is consistently assigned across frames. Below is a simple example:
 
@@ -214,7 +214,7 @@ To implement object tracking, use the `model.track` method and ensure that each
 
         track_history = defaultdict(lambda: [])
 
-        model = YOLO("yolov8n-seg.pt")  # segmentation model
+        model = YOLO("yolo11n-seg.pt")  # segmentation model
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
 
@@ -247,6 +247,6 @@ To implement object tracking, use the `model.track` method and ensure that each
 
 Find more in the [Instance Segmentation and Tracking section](#samples).
 
-### Are there any datasets provided by Ultralytics suitable for training YOLOv8 models for instance segmentation and tracking?
+### Are there any datasets provided by Ultralytics suitable for training YOLO11 models for instance segmentation and tracking?
 
-Yes, Ultralytics offers several datasets suitable for training YOLOv8 models, including segmentation and tracking datasets. Dataset examples, structures, and instructions for use can be found in the [Ultralytics Datasets documentation](https://docs.ultralytics.com/datasets/).
+Yes, Ultralytics offers several datasets suitable for training YOLO11 models, including segmentation and tracking datasets. Dataset examples, structures, and instructions for use can be found in the [Ultralytics Datasets documentation](https://docs.ultralytics.com/datasets/).

docs/en/guides/isolating-segmentation-objects.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Learn to extract isolated objects from inference results using Ultralytics Predict Mode. Step-by-step guide for segmentation object isolation.
-keywords: Ultralytics, segmentation, object isolation, Predict Mode, YOLOv8, machine learning, object detection, binary mask, image processing
+keywords: Ultralytics, segmentation, object isolation, Predict Mode, YOLO11, machine learning, object detection, binary mask, image processing
 ---
 
 # Isolating Segmentation Objects
@@ -24,7 +24,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
     from ultralytics import YOLO
 
     # Load a model
-    model = YOLO("yolov8n-seg.pt")
+    model = YOLO("yolo11n-seg.pt")
 
     # Run inference
     results = model.predict()
@@ -267,7 +267,7 @@ import numpy as np
 
 from ultralytics import YOLO
 
-m = YOLO("yolov8n-seg.pt")  # (4)!
+m = YOLO("yolo11n-seg.pt")  # (4)!
 res = m.predict()  # (3)!
 
 # Iterate detection results (5)
@@ -310,16 +310,16 @@ for r in res:
 
 ## FAQ
 
-### How do I isolate objects using Ultralytics YOLOv8 for segmentation tasks?
+### How do I isolate objects using Ultralytics YOLO11 for segmentation tasks?
 
-To isolate objects using Ultralytics YOLOv8, follow these steps:
+To isolate objects using Ultralytics YOLO11, follow these steps:
 
 1. **Load the model and run inference:**
 
     ```python
     from ultralytics import YOLO
 
-    model = YOLO("yolov8n-seg.pt")
+    model = YOLO("yolo11n-seg.pt")
     results = model.predict(source="path/to/your/image.jpg")
     ```
 
@@ -345,7 +345,7 @@ Refer to the guide on [Predict Mode](../modes/predict.md) and the [Segment Task]
 
 ### What options are available for saving the isolated objects after segmentation?
 
-Ultralytics YOLOv8 offers two main options for saving isolated objects:
+Ultralytics YOLO11 offers two main options for saving isolated objects:
 
 1. **With a Black Background:**
 
@@ -361,7 +361,7 @@ Ultralytics YOLOv8 offers two main options for saving isolated objects:
 
 For further details, visit the [Predict Mode](../modes/predict.md) section.
 
-### How can I crop isolated objects to their bounding boxes using Ultralytics YOLOv8?
+### How can I crop isolated objects to their bounding boxes using Ultralytics YOLO11?
 
 To crop isolated objects to their bounding boxes:
 
@@ -378,9 +378,9 @@ To crop isolated objects to their bounding boxes:
 
 Learn more about bounding box results in the [Predict Mode](../modes/predict.md#boxes) documentation.
 
-### Why should I use Ultralytics YOLOv8 for object isolation in segmentation tasks?
+### Why should I use Ultralytics YOLO11 for object isolation in segmentation tasks?
 
-Ultralytics YOLOv8 provides:
+Ultralytics YOLO11 provides:
 
 - **High-speed** real-time object detection and segmentation.
 - **Accurate bounding box and mask generation** for precise object isolation.
@@ -388,9 +388,9 @@ Ultralytics YOLOv8 provides:
 
 Explore the benefits of using YOLO in the [Segment Task documentation](../tasks/segment.md).
 
-### Can I save isolated objects including the background using Ultralytics YOLOv8?
+### Can I save isolated objects including the background using Ultralytics YOLO11?
 
-Yes, this is a built-in feature in Ultralytics YOLOv8. Use the `save_crop` argument in the `predict()` method. For example:
+Yes, this is a built-in feature in Ultralytics YOLO11. Use the `save_crop` argument in the `predict()` method. For example:
 
 ```python
 results = model.predict(source="path/to/your/image.jpg", save_crop=True)

docs/en/guides/model-deployment-options.md

@@ -1,26 +1,26 @@
 ---
 comments: true
-description: Learn about YOLOv8's diverse deployment options to maximize your model's performance. Explore PyTorch, TensorRT, OpenVINO, TF Lite, and more!.
-keywords: YOLOv8, deployment options, export formats, PyTorch, TensorRT, OpenVINO, TF Lite, machine learning, model deployment
+description: Learn about YOLO11's diverse deployment options to maximize your model's performance. Explore PyTorch, TensorRT, OpenVINO, TF Lite, and more!.
+keywords: YOLO11, deployment options, export formats, PyTorch, TensorRT, OpenVINO, TF Lite, machine learning, model deployment
 ---
 
-# Understanding YOLOv8's Deployment Options
+# Understanding YOLO11's Deployment Options
 
 ## Introduction
 
-You've come a long way on your journey with YOLOv8. You've diligently collected data, meticulously annotated it, and put in the hours to train and rigorously evaluate your custom YOLOv8 model. Now, it's time to put your model to work for your specific application, use case, or project. But there's a critical decision that stands before you: how to export and deploy your model effectively.
+You've come a long way on your journey with YOLO11. You've diligently collected data, meticulously annotated it, and put in the hours to train and rigorously evaluate your custom YOLO11 model. Now, it's time to put your model to work for your specific application, use case, or project. But there's a critical decision that stands before you: how to export and deploy your model effectively.
 
-This guide walks you through YOLOv8's deployment options and the essential factors to consider to choose the right option for your project.
+This guide walks you through YOLO11's deployment options and the essential factors to consider to choose the right option for your project.
 
-## How to Select the Right Deployment Option for Your YOLOv8 Model
+## How to Select the Right Deployment Option for Your YOLO11 Model
 
-When it's time to deploy your YOLOv8 model, selecting a suitable export format is very important. As outlined in the [Ultralytics YOLOv8 Modes documentation](../modes/export.md#usage-examples), the model.export() function allows for converting your trained model into a variety of formats tailored to diverse environments and performance requirements.
+When it's time to deploy your YOLO11 model, selecting a suitable export format is very important. As outlined in the [Ultralytics YOLO11 Modes documentation](../modes/export.md#usage-examples), the model.export() function allows for converting your trained model into a variety of formats tailored to diverse environments and performance requirements.
 
 The ideal format depends on your model's intended operational context, balancing speed, hardware constraints, and ease of integration. In the following section, we'll take a closer look at each export option, understanding when to choose each one.
 
-### YOLOv8's Deployment Options
+### YOLO11's Deployment Options
 
-Let's walk through the different YOLOv8 deployment options. For a detailed walkthrough of the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
+Let's walk through the different YOLO11 deployment options. For a detailed walkthrough of the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
 
 #### PyTorch
 
@@ -258,9 +258,9 @@ NCNN is a high-performance neural network inference framework optimized for the
 
 - **Hardware Acceleration**: Tailored for ARM CPUs and GPUs, with specific optimizations for these architectures.
 
-## Comparative Analysis of YOLOv8 Deployment Options
+## Comparative Analysis of YOLO11 Deployment Options
 
-The following table provides a snapshot of the various deployment options available for YOLOv8 models, helping you to assess which may best fit your project needs based on several critical criteria. For an in-depth look at each deployment option's format, please see the [Ultralytics documentation page on export formats](../modes/export.md#export-formats).
+The following table provides a snapshot of the various deployment options available for YOLO11 models, helping you to assess which may best fit your project needs based on several critical criteria. For an in-depth look at each deployment option's format, please see the [Ultralytics documentation page on export formats](../modes/export.md#export-formats).
 
 | Deployment Option | Performance Benchmarks                          | Compatibility and Integration                  | Community Support and Ecosystem               | Case Studies                               | Maintenance and Updates                     | Security Considerations                           | Hardware Acceleration              |
 | ----------------- | ----------------------------------------------- | ---------------------------------------------- | --------------------------------------------- | ------------------------------------------ | ------------------------------------------- | ------------------------------------------------- | ---------------------------------- |
@@ -282,33 +282,33 @@ This comparative analysis gives you a high-level overview. For deployment, it's
 
 ## Community and Support
 
-When you're getting started with YOLOv8, having a helpful community and support can make a significant impact. Here's how to connect with others who share your interests and get the assistance you need.
+When you're getting started with YOLO11, having a helpful community and support can make a significant impact. Here's how to connect with others who share your interests and get the assistance you need.
 
 ### Engage with the Broader Community
 
-- **GitHub Discussions:** The YOLOv8 repository on GitHub has a "Discussions" section where you can ask questions, report issues, and suggest improvements.
+- **GitHub Discussions:** The YOLO11 repository on GitHub has a "Discussions" section where you can ask questions, report issues, and suggest improvements.
 
 - **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and developers.
 
 ### Official Documentation and Resources
 
-- **Ultralytics YOLOv8 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
+- **Ultralytics YOLO11 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLO11, along with guides on installation, usage, and troubleshooting.
 
-These resources will help you tackle challenges and stay updated on the latest trends and best practices in the YOLOv8 community.
+These resources will help you tackle challenges and stay updated on the latest trends and best practices in the YOLO11 community.
 
 ## Conclusion
 
-In this guide, we've explored the different deployment options for YOLOv8. We've also discussed the important factors to consider when making your choice. These options allow you to customize your model for various environments and performance requirements, making it suitable for real-world applications.
+In this guide, we've explored the different deployment options for YOLO11. We've also discussed the important factors to consider when making your choice. These options allow you to customize your model for various environments and performance requirements, making it suitable for real-world applications.
 
-Don't forget that the YOLOv8 and Ultralytics community is a valuable source of help. Connect with other developers and experts to learn unique tips and solutions you might not find in regular documentation. Keep seeking knowledge, exploring new ideas, and sharing your experiences.
+Don't forget that the YOLO11 and Ultralytics community is a valuable source of help. Connect with other developers and experts to learn unique tips and solutions you might not find in regular documentation. Keep seeking knowledge, exploring new ideas, and sharing your experiences.
 
 Happy deploying!
 
 ## FAQ
 
-### What are the deployment options available for YOLOv8 on different hardware platforms?
+### What are the deployment options available for YOLO11 on different hardware platforms?
 
-Ultralytics YOLOv8 supports various deployment formats, each designed for specific environments and hardware platforms. Key formats include:
+Ultralytics YOLO11 supports various deployment formats, each designed for specific environments and hardware platforms. Key formats include:
 
 - **PyTorch** for research and prototyping, with excellent Python integration.
 - **TorchScript** for production environments where Python is unavailable.
@@ -318,18 +318,18 @@ Ultralytics YOLOv8 supports various deployment formats, each designed for specif
 
 Each format has unique advantages. For a detailed walkthrough, see our [export process documentation](../modes/export.md#usage-examples).
 
-### How do I improve the inference speed of my YOLOv8 model on an Intel CPU?
+### How do I improve the inference speed of my YOLO11 model on an Intel CPU?
 
-To enhance inference speed on Intel CPUs, you can deploy your YOLOv8 model using Intel's OpenVINO toolkit. OpenVINO offers significant performance boosts by optimizing models to leverage Intel hardware efficiently.
+To enhance inference speed on Intel CPUs, you can deploy your YOLO11 model using Intel's OpenVINO toolkit. OpenVINO offers significant performance boosts by optimizing models to leverage Intel hardware efficiently.
 
-1. Convert your YOLOv8 model to the OpenVINO format using the `model.export()` function.
+1. Convert your YOLO11 model to the OpenVINO format using the `model.export()` function.
 2. Follow the detailed setup guide in the [Intel OpenVINO Export documentation](../integrations/openvino.md).
 
 For more insights, check out our [blog post](https://www.ultralytics.com/blog/achieve-faster-inference-speeds-ultralytics-yolov8-openvino).
 
-### Can I deploy YOLOv8 models on mobile devices?
+### Can I deploy YOLO11 models on mobile devices?
 
-Yes, YOLOv8 models can be deployed on mobile devices using [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) Lite (TF Lite) for both Android and iOS platforms. TF Lite is designed for mobile and embedded devices, providing efficient on-device inference.
+Yes, YOLO11 models can be deployed on mobile devices using [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) Lite (TF Lite) for both Android and iOS platforms. TF Lite is designed for mobile and embedded devices, providing efficient on-device inference.
 
 !!! example
 
@@ -349,9 +349,9 @@ Yes, YOLOv8 models can be deployed on mobile devices using [TensorFlow](https://
 
 For more details on deploying models to mobile, refer to our [TF Lite integration guide](../integrations/tflite.md).
 
-### What factors should I consider when choosing a deployment format for my YOLOv8 model?
+### What factors should I consider when choosing a deployment format for my YOLO11 model?
 
-When choosing a deployment format for YOLOv8, consider the following factors:
+When choosing a deployment format for YOLO11, consider the following factors:
 
 - **Performance**: Some formats like TensorRT provide exceptional speeds on NVIDIA GPUs, while OpenVINO is optimized for Intel hardware.
 - **Compatibility**: ONNX offers broad compatibility across different platforms.
@@ -360,11 +360,11 @@ When choosing a deployment format for YOLOv8, consider the following factors:
 
 For a comparative analysis, refer to our [export formats documentation](../modes/export.md#export-formats).
 
-### How can I deploy YOLOv8 models in a web application?
+### How can I deploy YOLO11 models in a web application?
 
-To deploy YOLOv8 models in a web application, you can use TensorFlow.js (TF.js), which allows for running [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models directly in the browser. This approach eliminates the need for backend infrastructure and provides real-time performance.
+To deploy YOLO11 models in a web application, you can use TensorFlow.js (TF.js), which allows for running [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models directly in the browser. This approach eliminates the need for backend infrastructure and provides real-time performance.
 
-1. Export the YOLOv8 model to the TF.js format.
+1. Export the YOLO11 model to the TF.js format.
 2. Integrate the exported model into your web application.
 
 For step-by-step instructions, refer to our guide on [TensorFlow.js integration](../integrations/tfjs.md).

docs/en/guides/model-deployment-practices.md

@@ -27,7 +27,7 @@ It's also important to follow best practices when deploying a model because depl
 
 Often times, once a model is [trained](./model-training-tips.md), [evaluated](./model-evaluation-insights.md), and [tested](./model-testing.md), it needs to be converted into specific formats to be deployed effectively in various environments, such as cloud, edge, or local devices.
 
-With respect to YOLOv8, you can [export your model](../modes/export.md) to different formats. For example, when you need to transfer your model between different frameworks, ONNX is an excellent tool and [exporting to YOLOv8 to ONNX](../integrations/onnx.md) is easy. You can check out more options about integrating your model into different environments smoothly and effectively [here](../integrations/index.md).
+With respect to YOLO11, you can [export your model](../modes/export.md) to different formats. For example, when you need to transfer your model between different frameworks, ONNX is an excellent tool and [exporting to YOLO11 to ONNX](../integrations/onnx.md) is easy. You can check out more options about integrating your model into different environments smoothly and effectively [here](../integrations/index.md).
 
 ### Choosing a Deployment Environment
 
@@ -94,7 +94,7 @@ Experiencing a drop in your model's accuracy after deployment can be frustrating
 - **Review Model Export and Conversion:** Re-export the model and make sure that the conversion process maintains the integrity of the model weights and architecture.
 - **Test with a Controlled Dataset:** Deploy the model in a test environment with a dataset you control and compare the results with the training phase. You can identify if the issue is with the deployment environment or the data.
 
-When deploying YOLOv8, several factors can affect model accuracy. Converting models to formats like [TensorRT](../integrations/tensorrt.md) involves optimizations such as weight quantization and layer fusion, which can cause minor precision losses. Using FP16 (half-precision) instead of FP32 (full-precision) can speed up inference but may introduce numerical precision errors. Also, hardware constraints, like those on the [Jetson Nano](./nvidia-jetson.md), with lower CUDA core counts and reduced memory bandwidth, can impact performance.
+When deploying YOLO11, several factors can affect model accuracy. Converting models to formats like [TensorRT](../integrations/tensorrt.md) involves optimizations such as weight quantization and layer fusion, which can cause minor precision losses. Using FP16 (half-precision) instead of FP32 (full-precision) can speed up inference but may introduce numerical precision errors. Also, hardware constraints, like those on the [Jetson Nano](./nvidia-jetson.md), with lower CUDA core counts and reduced memory bandwidth, can impact performance.
 
 ### Inferences Are Taking Longer Than You Expected
 
@@ -106,7 +106,7 @@ When deploying [machine learning](https://www.ultralytics.com/glossary/machine-l
 - **Profile the Inference Pipeline:** Identifying bottlenecks in the inference pipeline can help pinpoint the source of delays. Use profiling tools to analyze each step of the inference process, identifying and addressing any stages that cause significant delays, such as inefficient layers or data transfer issues.
 - **Use Appropriate Precision:** Using higher precision than necessary can slow down inference times. Experiment with using lower precision, such as FP16 (half-precision), instead of FP32 (full-precision). While FP16 can reduce inference time, also keep in mind that it can impact model accuracy.
 
-If you are facing this issue while deploying YOLOv8, consider that YOLOv8 offers [various model sizes](../models/yolov8.md), such as YOLOv8n (nano) for devices with lower memory capacity and YOLOv8x (extra-large) for more powerful GPUs. Choosing the right model variant for your hardware can help balance memory usage and processing time.
+If you are facing this issue while deploying YOLO11, consider that YOLO11 offers [various model sizes](../models/yolov8.md), such as YOLO11n (nano) for devices with lower memory capacity and YOLOv8x (extra-large) for more powerful GPUs. Choosing the right model variant for your hardware can help balance memory usage and processing time.
 
 Also keep in mind that the size of the input images directly impacts memory usage and processing time. Lower resolutions reduce memory usage and speed up inference, while higher resolutions improve accuracy but require more memory and processing power.
 
@@ -132,12 +132,12 @@ Being part of a community of computer vision enthusiasts can help you solve prob
 
 ### Community Resources
 
-- **GitHub Issues:** Explore the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
+- **GitHub Issues:** Explore the [YOLO11 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
 - **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Visit the [official YOLOv8 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
+- **Ultralytics YOLO11 Documentation:** Visit the [official YOLO11 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
 
 Using these resources will help you solve challenges and stay up-to-date with the latest trends and practices in the computer vision community.
 
@@ -149,22 +149,22 @@ After deploying your model, the next step would be monitoring, maintaining, and
 
 ## FAQ
 
-### What are the best practices for deploying a machine learning model using Ultralytics YOLOv8?
+### What are the best practices for deploying a machine learning model using Ultralytics YOLO11?
 
-Deploying a machine learning model, particularly with Ultralytics YOLOv8, involves several best practices to ensure efficiency and reliability. First, choose the deployment environment that suits your needs—cloud, edge, or local. Optimize your model through techniques like [pruning, quantization, and knowledge distillation](#model-optimization-techniques) for efficient deployment in resource-constrained environments. Lastly, ensure data consistency and preprocessing steps align with the training phase to maintain performance. You can also refer to [model deployment options](./model-deployment-options.md) for more detailed guidelines.
+Deploying a machine learning model, particularly with Ultralytics YOLO11, involves several best practices to ensure efficiency and reliability. First, choose the deployment environment that suits your needs—cloud, edge, or local. Optimize your model through techniques like [pruning, quantization, and knowledge distillation](#model-optimization-techniques) for efficient deployment in resource-constrained environments. Lastly, ensure data consistency and preprocessing steps align with the training phase to maintain performance. You can also refer to [model deployment options](./model-deployment-options.md) for more detailed guidelines.
 
-### How can I troubleshoot common deployment issues with Ultralytics YOLOv8 models?
+### How can I troubleshoot common deployment issues with Ultralytics YOLO11 models?
 
 Troubleshooting deployment issues can be broken down into a few key steps. If your model's accuracy drops after deployment, check for data consistency, validate preprocessing steps, and ensure the hardware/software environment matches what you used during training. For slow inference times, perform warm-up runs, optimize your inference engine, use asynchronous processing, and profile your inference pipeline. Refer to [troubleshooting deployment issues](#troubleshooting-deployment-issues) for a detailed guide on these best practices.
 
-### How does Ultralytics YOLOv8 optimization enhance model performance on edge devices?
+### How does Ultralytics YOLO11 optimization enhance model performance on edge devices?
 
-Optimizing Ultralytics YOLOv8 models for edge devices involves using techniques like pruning to reduce the model size, quantization to convert weights to lower precision, and knowledge distillation to train smaller models that mimic larger ones. These techniques ensure the model runs efficiently on devices with limited computational power. Tools like [TensorFlow Lite](../integrations/tflite.md) and [NVIDIA Jetson](./nvidia-jetson.md) are particularly useful for these optimizations. Learn more about these techniques in our section on [model optimization](#model-optimization-techniques).
+Optimizing Ultralytics YOLO11 models for edge devices involves using techniques like pruning to reduce the model size, quantization to convert weights to lower precision, and knowledge distillation to train smaller models that mimic larger ones. These techniques ensure the model runs efficiently on devices with limited computational power. Tools like [TensorFlow Lite](../integrations/tflite.md) and [NVIDIA Jetson](./nvidia-jetson.md) are particularly useful for these optimizations. Learn more about these techniques in our section on [model optimization](#model-optimization-techniques).
 
-### What are the security considerations for deploying machine learning models with Ultralytics YOLOv8?
+### What are the security considerations for deploying machine learning models with Ultralytics YOLO11?
 
 Security is paramount when deploying machine learning models. Ensure secure data transmission using encryption protocols like TLS. Implement robust access controls, including strong authentication and role-based access control (RBAC). Model obfuscation techniques, such as encrypting model parameters and serving models in a secure environment like a trusted execution environment (TEE), offer additional protection. For detailed practices, refer to [security considerations](#security-considerations-in-model-deployment).
 
-### How do I choose the right deployment environment for my Ultralytics YOLOv8 model?
+### How do I choose the right deployment environment for my Ultralytics YOLO11 model?
 
-Selecting the optimal deployment environment for your Ultralytics YOLOv8 model depends on your application's specific needs. Cloud deployment offers scalability and ease of access, making it ideal for applications with high data volumes. Edge deployment is best for low-latency applications requiring real-time responses, using tools like [TensorFlow Lite](../integrations/tflite.md). Local deployment suits scenarios needing stringent data privacy and control. For a comprehensive overview of each environment, check out our section on [choosing a deployment environment](#choosing-a-deployment-environment).
+Selecting the optimal deployment environment for your Ultralytics YOLO11 model depends on your application's specific needs. Cloud deployment offers scalability and ease of access, making it ideal for applications with high data volumes. Edge deployment is best for low-latency applications requiring real-time responses, using tools like [TensorFlow Lite](../integrations/tflite.md). Local deployment suits scenarios needing stringent data privacy and control. For a comprehensive overview of each environment, check out our section on [choosing a deployment environment](#choosing-a-deployment-environment).

docs/en/guides/model-evaluation-insights.md

@@ -1,6 +1,6 @@
 ---
 comments: true
-description: Explore the most effective ways to assess and refine YOLOv8 models for better performance. Learn about evaluation metrics, fine-tuning processes, and how to customize your model for specific needs.
+description: Explore the most effective ways to assess and refine YOLO11 models for better performance. Learn about evaluation metrics, fine-tuning processes, and how to customize your model for specific needs.
 keywords: Model Evaluation, Machine Learning Model Evaluation, Fine Tuning Machine Learning, Fine Tune Model, Evaluating Models, Model Fine Tuning, How to Fine Tune a Model
 ---
 
@@ -45,23 +45,23 @@ Other mAP metrics include mAP@0.75, which uses a stricter IoU threshold of 0.75,
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/mean-average-precision-overview.avif" alt="Mean Average Precision Overview">
 </p>
 
-## Evaluating YOLOv8 Model Performance
+## Evaluating YOLO11 Model Performance
 
-With respect to YOLOv8, you can use the [validation mode](../modes/val.md) to evaluate the model. Also, be sure to take a look at our guide that goes in-depth into [YOLOv8 performance metrics](./yolo-performance-metrics.md) and how they can be interpreted.
+With respect to YOLO11, you can use the [validation mode](../modes/val.md) to evaluate the model. Also, be sure to take a look at our guide that goes in-depth into [YOLO11 performance metrics](./yolo-performance-metrics.md) and how they can be interpreted.
 
 ### Common Community Questions
 
-When evaluating your YOLOv8 model, you might run into a few hiccups. Based on common community questions, here are some tips to help you get the most out of your YOLOv8 model:
+When evaluating your YOLO11 model, you might run into a few hiccups. Based on common community questions, here are some tips to help you get the most out of your YOLO11 model:
 
 #### Handling Variable Image Sizes
 
-Evaluating your YOLOv8 model with images of different sizes can help you understand its performance on diverse datasets. Using the `rect=true` validation parameter, YOLOv8 adjusts the network's stride for each batch based on the image sizes, allowing the model to handle rectangular images without forcing them to a single size.
+Evaluating your YOLO11 model with images of different sizes can help you understand its performance on diverse datasets. Using the `rect=true` validation parameter, YOLO11 adjusts the network's stride for each batch based on the image sizes, allowing the model to handle rectangular images without forcing them to a single size.
 
 The `imgsz` validation parameter sets the maximum dimension for image resizing, which is 640 by default. You can adjust this based on your dataset's maximum dimensions and the GPU memory available. Even with `imgsz` set, `rect=true` lets the model manage varying image sizes effectively by dynamically adjusting the stride.
 
-#### Accessing YOLOv8 Metrics
+#### Accessing YOLO11 Metrics
 
-If you want to get a deeper understanding of your YOLOv8 model's performance, you can easily access specific evaluation metrics with a few lines of Python code. The code snippet below will let you load your model, run an evaluation, and print out various metrics that show how well your model is doing.
+If you want to get a deeper understanding of your YOLO11 model's performance, you can easily access specific evaluation metrics with a few lines of Python code. The code snippet below will let you load your model, run an evaluation, and print out various metrics that show how well your model is doing.
 
 !!! example "Usage"
 
@@ -71,7 +71,7 @@ If you want to get a deeper understanding of your YOLOv8 model's performance, yo
         from ultralytics import YOLO
 
         # Load the model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Run the evaluation
         results = model.val(data="coco8.yaml")
@@ -101,7 +101,7 @@ If you want to get a deeper understanding of your YOLOv8 model's performance, yo
         print("Recall curve:", results.box.r_curve)
         ```
 
-The results object also includes speed metrics like preprocess time, inference time, loss, and postprocess time. By analyzing these metrics, you can fine-tune and optimize your YOLOv8 model for better performance, making it more effective for your specific use case.
+The results object also includes speed metrics like preprocess time, inference time, loss, and postprocess time. By analyzing these metrics, you can fine-tune and optimize your YOLO11 model for better performance, making it more effective for your specific use case.
 
 ## How Does Fine-Tuning Work?
 
@@ -115,11 +115,11 @@ Fine-tuning a model means paying close attention to several vital parameters and
 
 Usually, during the initial training [epochs](https://www.ultralytics.com/glossary/epoch), the learning rate starts low and gradually increases to stabilize the training process. However, since your model has already learned some features from the previous dataset, starting with a higher learning rate right away can be more beneficial.
 
-When evaluating your YOLOv8 model, you can set the `warmup_epochs` validation parameter to `warmup_epochs=0` to prevent the learning rate from starting too high. By following this process, the training will continue from the provided weights, adjusting to the nuances of your new data.
+When evaluating your YOLO11 model, you can set the `warmup_epochs` validation parameter to `warmup_epochs=0` to prevent the learning rate from starting too high. By following this process, the training will continue from the provided weights, adjusting to the nuances of your new data.
 
 ### Image Tiling for Small Objects
 
-Image tiling can improve detection accuracy for small objects. By dividing larger images into smaller segments, such as splitting 1280x1280 images into multiple 640x640 segments, you maintain the original resolution, and the model can learn from high-resolution fragments. When using YOLOv8, make sure to adjust your labels for these new segments correctly.
+Image tiling can improve detection accuracy for small objects. By dividing larger images into smaller segments, such as splitting 1280x1280 images into multiple 640x640 segments, you maintain the original resolution, and the model can learn from high-resolution fragments. When using YOLO11, make sure to adjust your labels for these new segments correctly.
 
 ## Engage with the Community
 
@@ -127,12 +127,12 @@ Sharing your ideas and questions with other [computer vision](https://www.ultral
 
 ### Finding Help and Support
 
-- **GitHub Issues:** Explore the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to ask questions, report bugs, and suggest features. The community and maintainers are available to assist with any issues you encounter.
+- **GitHub Issues:** Explore the YOLO11 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to ask questions, report bugs, and suggest features. The community and maintainers are available to assist with any issues you encounter.
 - **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Check out the [official YOLOv8 documentation](./index.md) for comprehensive guides and valuable insights on various computer vision tasks and projects.
+- **Ultralytics YOLO11 Documentation:** Check out the [official YOLO11 documentation](./index.md) for comprehensive guides and valuable insights on various computer vision tasks and projects.
 
 ## Final Thoughts
 
@@ -140,30 +140,30 @@ Evaluating and fine-tuning your computer vision model are important steps for su
 
 ## FAQ
 
-### What are the key metrics for evaluating YOLOv8 model performance?
+### What are the key metrics for evaluating YOLO11 model performance?
 
-To evaluate YOLOv8 model performance, important metrics include Confidence Score, Intersection over Union (IoU), and Mean Average Precision (mAP). Confidence Score measures the model's certainty for each detected object class. IoU evaluates how well the predicted bounding box overlaps with the ground truth. Mean Average Precision (mAP) aggregates precision scores across classes, with mAP@.5 and mAP@.5:.95 being two common types for varying IoU thresholds. Learn more about these metrics in our [YOLOv8 performance metrics guide](./yolo-performance-metrics.md).
+To evaluate YOLO11 model performance, important metrics include Confidence Score, Intersection over Union (IoU), and Mean Average Precision (mAP). Confidence Score measures the model's certainty for each detected object class. IoU evaluates how well the predicted bounding box overlaps with the ground truth. Mean Average Precision (mAP) aggregates precision scores across classes, with mAP@.5 and mAP@.5:.95 being two common types for varying IoU thresholds. Learn more about these metrics in our [YOLO11 performance metrics guide](./yolo-performance-metrics.md).
 
-### How can I fine-tune a pre-trained YOLOv8 model for my specific dataset?
+### How can I fine-tune a pre-trained YOLO11 model for my specific dataset?
 
-Fine-tuning a pre-trained YOLOv8 model involves adjusting its parameters to improve performance on a specific task or dataset. Start by evaluating your model using metrics, then set a higher initial learning rate by adjusting the `warmup_epochs` parameter to 0 for immediate stability. Use parameters like `rect=true` for handling varied image sizes effectively. For more detailed guidance, refer to our section on [fine-tuning YOLOv8 models](#how-does-fine-tuning-work).
+Fine-tuning a pre-trained YOLO11 model involves adjusting its parameters to improve performance on a specific task or dataset. Start by evaluating your model using metrics, then set a higher initial learning rate by adjusting the `warmup_epochs` parameter to 0 for immediate stability. Use parameters like `rect=true` for handling varied image sizes effectively. For more detailed guidance, refer to our section on [fine-tuning YOLO11 models](#how-does-fine-tuning-work).
 
-### How can I handle variable image sizes when evaluating my YOLOv8 model?
+### How can I handle variable image sizes when evaluating my YOLO11 model?
 
-To handle variable image sizes during evaluation, use the `rect=true` parameter in YOLOv8, which adjusts the network's stride for each batch based on image sizes. The `imgsz` parameter sets the maximum dimension for image resizing, defaulting to 640. Adjust `imgsz` to suit your dataset and GPU memory. For more details, visit our [section on handling variable image sizes](#handling-variable-image-sizes).
+To handle variable image sizes during evaluation, use the `rect=true` parameter in YOLO11, which adjusts the network's stride for each batch based on image sizes. The `imgsz` parameter sets the maximum dimension for image resizing, defaulting to 640. Adjust `imgsz` to suit your dataset and GPU memory. For more details, visit our [section on handling variable image sizes](#handling-variable-image-sizes).
 
-### What practical steps can I take to improve mean average precision for my YOLOv8 model?
+### What practical steps can I take to improve mean average precision for my YOLO11 model?
 
-Improving mean average precision (mAP) for a YOLOv8 model involves several steps:
+Improving mean average precision (mAP) for a YOLO11 model involves several steps:
 
 1. **Tuning Hyperparameters**: Experiment with different learning rates, [batch sizes](https://www.ultralytics.com/glossary/batch-size), and image augmentations.
 2. **[Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation)**: Use techniques like Mosaic and MixUp to create diverse training samples.
 3. **Image Tiling**: Split larger images into smaller tiles to improve detection accuracy for small objects.
    Refer to our detailed guide on [model fine-tuning](#tips-for-fine-tuning-your-model) for specific strategies.
 
-### How do I access YOLOv8 model evaluation metrics in Python?
+### How do I access YOLO11 model evaluation metrics in Python?
 
-You can access YOLOv8 model evaluation metrics using Python with the following steps:
+You can access YOLO11 model evaluation metrics using Python with the following steps:
 
 !!! example "Usage"
 
@@ -173,7 +173,7 @@ You can access YOLOv8 model evaluation metrics using Python with the following s
         from ultralytics import YOLO
 
         # Load the model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Run the evaluation
         results = model.val(data="coco8.yaml")
@@ -185,4 +185,4 @@ You can access YOLOv8 model evaluation metrics using Python with the following s
         print("Mean recall:", results.box.mr)
         ```
 
-Analyzing these metrics helps fine-tune and optimize your YOLOv8 model. For a deeper dive, check out our guide on [YOLOv8 metrics](../modes/val.md).
+Analyzing these metrics helps fine-tune and optimize your YOLO11 model. For a deeper dive, check out our guide on [YOLO11 metrics](../modes/val.md).

docs/en/guides/model-monitoring-and-maintenance.md

@@ -123,12 +123,12 @@ Joining a community of computer vision enthusiasts can help you solve problems a
 
 ### Community Resources
 
-- **GitHub Issues:** Check out the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are highly active and supportive.
+- **GitHub Issues:** Check out the [YOLO11 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are highly active and supportive.
 - **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Visit the [official YOLOv8 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
+- **Ultralytics YOLO11 Documentation:** Visit the [official YOLO11 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
 
 Using these resources will help you solve challenges and stay up-to-date with the latest trends and practices in the computer vision community.
 

docs/en/guides/model-testing.md

@@ -44,22 +44,22 @@ Next, the testing results can be analyzed:
 - **Error Analysis:** Perform a thorough error analysis to understand the types of errors (e.g., false positives vs. false negatives) and their potential causes.
 - **Bias and Fairness:** Check for any biases in the model's predictions. Ensure that the model performs equally well across different subsets of the data, especially if it includes sensitive attributes like race, gender, or age.
 
-## Testing Your YOLOv8 Model
+## Testing Your YOLO11 Model
 
-To test your YOLOv8 model, you can use the validation mode. It's a straightforward way to understand the model's strengths and areas that need improvement. Also, you'll need to format your test dataset correctly for YOLOv8. For more details on how to use the validation mode, check out the [Model Validation](../modes/val.md) docs page.
+To test your YOLO11 model, you can use the validation mode. It's a straightforward way to understand the model's strengths and areas that need improvement. Also, you'll need to format your test dataset correctly for YOLO11. For more details on how to use the validation mode, check out the [Model Validation](../modes/val.md) docs page.
 
-## Using YOLOv8 to Predict on Multiple Test Images
+## Using YOLO11 to Predict on Multiple Test Images
 
-If you want to test your trained YOLOv8 model on multiple images stored in a folder, you can easily do so in one go. Instead of using the validation mode, which is typically used to evaluate model performance on a validation set and provide detailed metrics, you might just want to see predictions on all images in your test set. For this, you can use the [prediction mode](../modes/predict.md).
+If you want to test your trained YOLO11 model on multiple images stored in a folder, you can easily do so in one go. Instead of using the validation mode, which is typically used to evaluate model performance on a validation set and provide detailed metrics, you might just want to see predictions on all images in your test set. For this, you can use the [prediction mode](../modes/predict.md).
 
 ### Difference Between Validation and Prediction Modes
 
 - **[Validation Mode](../modes/val.md):** Used to evaluate the model's performance by comparing predictions against known labels (ground truth). It provides detailed metrics such as accuracy, precision, recall, and F1 score.
 - **[Prediction Mode](../modes/predict.md):** Used to run the model on new, unseen data to generate predictions. It does not provide detailed performance metrics but allows you to see how the model performs on real-world images.
 
-## Running YOLOv8 Predictions Without Custom Training
+## Running YOLO11 Predictions Without Custom Training
 
-If you are interested in testing the basic YOLOv8 model to understand whether it can be used for your application without custom training, you can use the prediction mode. While the model is pre-trained on datasets like COCO, running predictions on your own dataset can give you a quick sense of how well it might perform in your specific context.
+If you are interested in testing the basic YOLO11 model to understand whether it can be used for your application without custom training, you can use the prediction mode. While the model is pre-trained on datasets like COCO, running predictions on your own dataset can give you a quick sense of how well it might perform in your specific context.
 
 ## Overfitting and [Underfitting](https://www.ultralytics.com/glossary/underfitting) in [Machine Learning](https://www.ultralytics.com/glossary/machine-learning-ml)
 
@@ -128,12 +128,12 @@ Becoming part of a community of computer vision enthusiasts can aid in solving p
 
 ### Community Resources
 
-- **GitHub Issues:** Explore the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
+- **GitHub Issues:** Explore the [YOLO11 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
 - **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Check out the [official YOLOv8 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
+- **Ultralytics YOLO11 Documentation:** Check out the [official YOLO11 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
 
 These resources will help you navigate challenges and remain updated on the latest trends and practices within the computer vision community.
 
@@ -147,9 +147,9 @@ Building trustworthy computer vision models relies on rigorous model testing. By
 
 Model evaluation and model testing are distinct steps in a computer vision project. Model evaluation involves using a labeled dataset to compute metrics such as [accuracy](https://www.ultralytics.com/glossary/accuracy), precision, recall, and [F1 score](https://www.ultralytics.com/glossary/f1-score), providing insights into the model's performance with a controlled dataset. Model testing, on the other hand, assesses the model's performance in real-world scenarios by applying it to new, unseen data, ensuring the model's learned behavior aligns with expectations outside the evaluation environment. For a detailed guide, refer to the [steps in a computer vision project](./steps-of-a-cv-project.md).
 
-### How can I test my Ultralytics YOLOv8 model on multiple images?
+### How can I test my Ultralytics YOLO11 model on multiple images?
 
-To test your Ultralytics YOLOv8 model on multiple images, you can use the [prediction mode](../modes/predict.md). This mode allows you to run the model on new, unseen data to generate predictions without providing detailed metrics. This is ideal for real-world performance testing on larger image sets stored in a folder. For evaluating performance metrics, use the [validation mode](../modes/val.md) instead.
+To test your Ultralytics YOLO11 model on multiple images, you can use the [prediction mode](../modes/predict.md). This mode allows you to run the model on new, unseen data to generate predictions without providing detailed metrics. This is ideal for real-world performance testing on larger image sets stored in a folder. For evaluating performance metrics, use the [validation mode](../modes/val.md) instead.
 
 ### What should I do if my computer vision model shows signs of overfitting or underfitting?
 
@@ -195,6 +195,6 @@ Post-testing, if the model performance meets the project goals, proceed with dep
 
 Gain insights from the [Model Testing Vs. Model Evaluation](#model-testing-vs-model-evaluation) section to refine and enhance model effectiveness in real-world applications.
 
-### How do I run YOLOv8 predictions without custom training?
+### How do I run YOLO11 predictions without custom training?
 
-You can run predictions using the pre-trained YOLOv8 model on your dataset to see if it suits your application needs. Utilize the [prediction mode](../modes/predict.md) to get a quick sense of performance results without diving into custom training.
+You can run predictions using the pre-trained YOLO11 model on your dataset to see if it suits your application needs. Utilize the [prediction mode](../modes/predict.md) to get a quick sense of performance results without diving into custom training.

docs/en/guides/model-training-tips.md

@@ -46,25 +46,25 @@ There are a few different aspects to think about when you are planning on using
 When training models on large datasets, efficiently utilizing your GPU is key. Batch size is an important factor. It is the number of data samples that a machine learning model processes in a single training iteration.
 Using the maximum batch size supported by your GPU, you can fully take advantage of its capabilities and reduce the time model training takes. However, you want to avoid running out of GPU memory. If you encounter memory errors, reduce the batch size incrementally until the model trains smoothly.
 
-With respect to YOLOv8, you can set the `batch_size` parameter in the [training configuration](../modes/train.md) to match your GPU capacity. Also, setting `batch=-1` in your training script will automatically determine the [batch size](https://www.ultralytics.com/glossary/batch-size) that can be efficiently processed based on your device's capabilities. By fine-tuning the batch size, you can make the most of your GPU resources and improve the overall training process.
+With respect to YOLO11, you can set the `batch_size` parameter in the [training configuration](../modes/train.md) to match your GPU capacity. Also, setting `batch=-1` in your training script will automatically determine the [batch size](https://www.ultralytics.com/glossary/batch-size) that can be efficiently processed based on your device's capabilities. By fine-tuning the batch size, you can make the most of your GPU resources and improve the overall training process.
 
 ### Subset Training
 
 Subset training is a smart strategy that involves training your model on a smaller set of data that represents the larger dataset. It can save time and resources, especially during initial model development and testing. If you are running short on time or experimenting with different model configurations, subset training is a good option.
 
-When it comes to YOLOv8, you can easily implement subset training by using the `fraction` parameter. This parameter lets you specify what fraction of your dataset to use for training. For example, setting `fraction=0.1` will train your model on 10% of the data. You can use this technique for quick iterations and tuning your model before committing to training a model using a full dataset. Subset training helps you make rapid progress and identify potential issues early on.
+When it comes to YOLO11, you can easily implement subset training by using the `fraction` parameter. This parameter lets you specify what fraction of your dataset to use for training. For example, setting `fraction=0.1` will train your model on 10% of the data. You can use this technique for quick iterations and tuning your model before committing to training a model using a full dataset. Subset training helps you make rapid progress and identify potential issues early on.
 
 ### Multi-scale Training
 
 Multiscale training is a technique that improves your model's ability to generalize by training it on images of varying sizes. Your model can learn to detect objects at different scales and distances and become more robust.
 
-For example, when you train YOLOv8, you can enable multiscale training by setting the `scale` parameter. This parameter adjusts the size of training images by a specified factor, simulating objects at different distances. For example, setting `scale=0.5` will reduce the image size by half, while `scale=2.0` will double it. Configuring this parameter allows your model to experience a variety of image scales and improve its detection capabilities across different object sizes and scenarios.
+For example, when you train YOLO11, you can enable multiscale training by setting the `scale` parameter. This parameter adjusts the size of training images by a specified factor, simulating objects at different distances. For example, setting `scale=0.5` will reduce the image size by half, while `scale=2.0` will double it. Configuring this parameter allows your model to experience a variety of image scales and improve its detection capabilities across different object sizes and scenarios.
 
 ### Caching
 
 Caching is an important technique to improve the efficiency of training machine learning models. By storing preprocessed images in memory, caching reduces the time the GPU spends waiting for data to be loaded from the disk. The model can continuously receive data without delays caused by disk I/O operations.
 
-Caching can be controlled when training YOLOv8 using the `cache` parameter:
+Caching can be controlled when training YOLO11 using the `cache` parameter:
 
 - _`cache=True`_: Stores dataset images in RAM, providing the fastest access speed but at the cost of increased memory usage.
 - _`cache='disk'`_: Stores the images on disk, slower than RAM but faster than loading fresh data each time.
@@ -80,19 +80,19 @@ Mixed precision training uses both 16-bit (FP16) and 32-bit (FP32) floating-poin
 
 To implement mixed precision training, you'll need to modify your training scripts and ensure your hardware (like GPUs) supports it. Many modern [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) frameworks, such as [Tensorflow](https://www.ultralytics.com/glossary/tensorflow), offer built-in support for mixed precision.
 
-Mixed precision training is straightforward when working with YOLOv8. You can use the `amp` flag in your training configuration. Setting `amp=True` enables Automatic Mixed Precision (AMP) training. Mixed precision training is a simple yet effective way to optimize your model training process.
+Mixed precision training is straightforward when working with YOLO11. You can use the `amp` flag in your training configuration. Setting `amp=True` enables Automatic Mixed Precision (AMP) training. Mixed precision training is a simple yet effective way to optimize your model training process.
 
 ### Pre-trained Weights
 
 Using pretrained weights is a smart way to speed up your model's training process. Pretrained weights come from models already trained on large datasets, giving your model a head start. [Transfer learning](https://www.ultralytics.com/glossary/transfer-learning) adapts pretrained models to new, related tasks. Fine-tuning a pre-trained model involves starting with these weights and then continuing training on your specific dataset. This method of training results in faster training times and often better performance because the model starts with a solid understanding of basic features.
 
-The `pretrained` parameter makes transfer learning easy with YOLOv8. Setting `pretrained=True` will use default pre-trained weights, or you can specify a path to a custom pre-trained model. Using pre-trained weights and transfer learning effectively boosts your model's capabilities and reduces training costs.
+The `pretrained` parameter makes transfer learning easy with YOLO11. Setting `pretrained=True` will use default pre-trained weights, or you can specify a path to a custom pre-trained model. Using pre-trained weights and transfer learning effectively boosts your model's capabilities and reduces training costs.
 
 ### Other Techniques to Consider When Handling a Large Dataset
 
 There are a couple of other techniques to consider when handling a large dataset:
 
-- **[Learning Rate](https://www.ultralytics.com/glossary/learning-rate) Schedulers**: Implementing learning rate schedulers dynamically adjusts the learning rate during training. A well-tuned learning rate can prevent the model from overshooting minima and improve stability. When training YOLOv8, the `lrf` parameter helps manage learning rate scheduling by setting the final learning rate as a fraction of the initial rate.
+- **[Learning Rate](https://www.ultralytics.com/glossary/learning-rate) Schedulers**: Implementing learning rate schedulers dynamically adjusts the learning rate during training. A well-tuned learning rate can prevent the model from overshooting minima and improve stability. When training YOLO11, the `lrf` parameter helps manage learning rate scheduling by setting the final learning rate as a fraction of the initial rate.
 - **Distributed Training**: For handling large datasets, distributed training can be a game-changer. You can reduce the training time by spreading the training workload across multiple GPUs or machines.
 
 ## The Number of Epochs To Train For
@@ -101,7 +101,7 @@ When training a model, an epoch refers to one complete pass through the entire t
 
 A common question that comes up is how to determine the number of epochs to train the model for. A good starting point is 300 epochs. If the model overfits early, you can reduce the number of epochs. If [overfitting](https://www.ultralytics.com/glossary/overfitting) does not occur after 300 epochs, you can extend the training to 600, 1200, or more epochs.
 
-However, the ideal number of epochs can vary based on your dataset's size and project goals. Larger datasets might require more epochs for the model to learn effectively, while smaller datasets might need fewer epochs to avoid overfitting. With respect to YOLOv8, you can set the `epochs` parameter in your training script.
+However, the ideal number of epochs can vary based on your dataset's size and project goals. Larger datasets might require more epochs for the model to learn effectively, while smaller datasets might need fewer epochs to avoid overfitting. With respect to YOLO11, you can set the `epochs` parameter in your training script.
 
 ## Early Stopping
 
@@ -113,7 +113,7 @@ The process involves setting a patience parameter that determines how many [epoc
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/early-stopping-overview.avif" alt="Early Stopping Overview">
 </p>
 
-For YOLOv8, you can enable early stopping by setting the patience parameter in your training configuration. For example, `patience=5` means training will stop if there's no improvement in validation metrics for 5 consecutive epochs. Using this method ensures the training process remains efficient and achieves optimal performance without excessive computation.
+For YOLO11, you can enable early stopping by setting the patience parameter in your training configuration. For example, `patience=5` means training will stop if there's no improvement in validation metrics for 5 consecutive epochs. Using this method ensures the training process remains efficient and achieves optimal performance without excessive computation.
 
 ## Choosing Between Cloud and Local Training
 
@@ -143,13 +143,13 @@ Different optimizers have various strengths and weaknesses. Let's take a glimpse
     - Combines the benefits of both SGD with momentum and RMSProp.
     - Adjusts the learning rate for each parameter based on estimates of the first and second moments of the gradients.
     - Well-suited for noisy data and sparse gradients.
-    - Efficient and generally requires less tuning, making it a recommended optimizer for YOLOv8.
+    - Efficient and generally requires less tuning, making it a recommended optimizer for YOLO11.
 
 - **RMSProp (Root Mean Square Propagation)**:
     - Adjusts the learning rate for each parameter by dividing the gradient by a running average of the magnitudes of recent gradients.
     - Helps in handling the vanishing gradient problem and is effective for [recurrent neural networks](https://www.ultralytics.com/glossary/recurrent-neural-network-rnn).
 
-For YOLOv8, the `optimizer` parameter lets you choose from various optimizers, including SGD, Adam, AdamW, NAdam, RAdam, and RMSProp, or you can set it to `auto` for automatic selection based on model configuration.
+For YOLO11, the `optimizer` parameter lets you choose from various optimizers, including SGD, Adam, AdamW, NAdam, RAdam, and RMSProp, or you can set it to `auto` for automatic selection based on model configuration.
 
 ## Connecting with the Community
 
@@ -157,12 +157,12 @@ Being part of a community of computer vision enthusiasts can help you solve prob
 
 ### Community Resources
 
-- **GitHub Issues:** Visit the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
+- **GitHub Issues:** Visit the [YOLO11 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
 - **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Check out the [official YOLOv8 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
+- **Ultralytics YOLO11 Documentation:** Check out the [official YOLO11 documentation](./index.md) for detailed guides and helpful tips on various computer vision projects.
 
 Using these resources will help you solve challenges and stay up-to-date with the latest trends and practices in the computer vision community.
 
@@ -174,20 +174,20 @@ Training computer vision models involves following good practices, optimizing yo
 
 ### How can I improve GPU utilization when training a large dataset with Ultralytics YOLO?
 
-To improve GPU utilization, set the `batch_size` parameter in your training configuration to the maximum size supported by your GPU. This ensures that you make full use of the GPU's capabilities, reducing training time. If you encounter memory errors, incrementally reduce the batch size until training runs smoothly. For YOLOv8, setting `batch=-1` in your training script will automatically determine the optimal batch size for efficient processing. For further information, refer to the [training configuration](../modes/train.md).
+To improve GPU utilization, set the `batch_size` parameter in your training configuration to the maximum size supported by your GPU. This ensures that you make full use of the GPU's capabilities, reducing training time. If you encounter memory errors, incrementally reduce the batch size until training runs smoothly. For YOLO11, setting `batch=-1` in your training script will automatically determine the optimal batch size for efficient processing. For further information, refer to the [training configuration](../modes/train.md).
 
-### What is mixed precision training, and how do I enable it in YOLOv8?
+### What is mixed precision training, and how do I enable it in YOLO11?
 
-Mixed precision training utilizes both 16-bit (FP16) and 32-bit (FP32) floating-point types to balance computational speed and precision. This approach speeds up training and reduces memory usage without sacrificing model [accuracy](https://www.ultralytics.com/glossary/accuracy). To enable mixed precision training in YOLOv8, set the `amp` parameter to `True` in your training configuration. This activates Automatic Mixed Precision (AMP) training. For more details on this optimization technique, see the [training configuration](../modes/train.md).
+Mixed precision training utilizes both 16-bit (FP16) and 32-bit (FP32) floating-point types to balance computational speed and precision. This approach speeds up training and reduces memory usage without sacrificing model [accuracy](https://www.ultralytics.com/glossary/accuracy). To enable mixed precision training in YOLO11, set the `amp` parameter to `True` in your training configuration. This activates Automatic Mixed Precision (AMP) training. For more details on this optimization technique, see the [training configuration](../modes/train.md).
 
-### How does multiscale training enhance YOLOv8 model performance?
+### How does multiscale training enhance YOLO11 model performance?
 
-Multiscale training enhances model performance by training on images of varying sizes, allowing the model to better generalize across different scales and distances. In YOLOv8, you can enable multiscale training by setting the `scale` parameter in the training configuration. For example, `scale=0.5` reduces the image size by half, while `scale=2.0` doubles it. This technique simulates objects at different distances, making the model more robust across various scenarios. For settings and more details, check out the [training configuration](../modes/train.md).
+Multiscale training enhances model performance by training on images of varying sizes, allowing the model to better generalize across different scales and distances. In YOLO11, you can enable multiscale training by setting the `scale` parameter in the training configuration. For example, `scale=0.5` reduces the image size by half, while `scale=2.0` doubles it. This technique simulates objects at different distances, making the model more robust across various scenarios. For settings and more details, check out the [training configuration](../modes/train.md).
 
-### How can I use pre-trained weights to speed up training in YOLOv8?
+### How can I use pre-trained weights to speed up training in YOLO11?
 
-Using pre-trained weights can significantly reduce training times and improve model performance by starting from a model that already understands basic features. In YOLOv8, you can set the `pretrained` parameter to `True` or specify a path to custom pre-trained weights in your training configuration. This approach, known as transfer learning, leverages knowledge from large datasets to adapt to your specific task. Learn more about pre-trained weights and their advantages [here](../modes/train.md).
+Using pre-trained weights can significantly reduce training times and improve model performance by starting from a model that already understands basic features. In YOLO11, you can set the `pretrained` parameter to `True` or specify a path to custom pre-trained weights in your training configuration. This approach, known as transfer learning, leverages knowledge from large datasets to adapt to your specific task. Learn more about pre-trained weights and their advantages [here](../modes/train.md).
 
-### What is the recommended number of epochs for training a model, and how do I set this in YOLOv8?
+### What is the recommended number of epochs for training a model, and how do I set this in YOLO11?
 
-The number of epochs refers to the complete passes through the training dataset during model training. A typical starting point is 300 epochs. If your model overfits early, you can reduce the number. Alternatively, if overfitting isn't observed, you might extend training to 600, 1200, or more epochs. To set this in YOLOv8, use the `epochs` parameter in your training script. For additional advice on determining the ideal number of epochs, refer to this section on [number of epochs](#the-number-of-epochs-to-train-for).
+The number of epochs refers to the complete passes through the training dataset during model training. A typical starting point is 300 epochs. If your model overfits early, you can reduce the number. Alternatively, if overfitting isn't observed, you might extend training to 600, 1200, or more epochs. To set this in YOLO11, use the `epochs` parameter in your training script. For additional advice on determining the ideal number of epochs, refer to this section on [number of epochs](#the-number-of-epochs-to-train-for).

docs/en/guides/object-blurring.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn how to use Ultralytics YOLOv8 for real-time object blurring to enhance privacy and focus in your images and videos.
-keywords: YOLOv8, object blurring, real-time processing, privacy protection, image manipulation, video editing, Ultralytics
+description: Learn how to use Ultralytics YOLO11 for real-time object blurring to enhance privacy and focus in your images and videos.
+keywords: YOLO11, object blurring, real-time processing, privacy protection, image manipulation, video editing, Ultralytics
 ---
 
-# Object Blurring using Ultralytics YOLOv8 🚀
+# Object Blurring using Ultralytics YOLO11 🚀
 
 ## What is Object Blurring?
 
-Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves applying a blurring effect to specific detected objects in an image or video. This can be achieved using the YOLOv8 model capabilities to identify and manipulate objects within a given scene.
+Object blurring with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves applying a blurring effect to specific detected objects in an image or video. This can be achieved using the YOLO11 model capabilities to identify and manipulate objects within a given scene.
 
 <p align="center">
   <br>
@@ -18,16 +18,16 @@ Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Object Blurring using Ultralytics YOLOv8
+  <strong>Watch:</strong> Object Blurring using Ultralytics YOLO11
 </p>
 
 ## Advantages of Object Blurring?
 
 - **Privacy Protection**: Object blurring is an effective tool for safeguarding privacy by concealing sensitive or personally identifiable information in images or videos.
-- **Selective Focus**: YOLOv8 allows for selective blurring, enabling users to target specific objects, ensuring a balance between privacy and retaining relevant visual information.
-- **Real-time Processing**: YOLOv8's efficiency enables object blurring in real-time, making it suitable for applications requiring on-the-fly privacy enhancements in dynamic environments.
+- **Selective Focus**: YOLO11 allows for selective blurring, enabling users to target specific objects, ensuring a balance between privacy and retaining relevant visual information.
+- **Real-time Processing**: YOLO11's efficiency enables object blurring in real-time, making it suitable for applications requiring on-the-fly privacy enhancements in dynamic environments.
 
-!!! example "Object Blurring using YOLOv8 Example"
+!!! example "Object Blurring using YOLO11 Example"
 
     === "Object Blurring"
 
@@ -37,7 +37,7 @@ Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         names = model.names
 
         cap = cv2.VideoCapture("path/to/video/file.mp4")
@@ -86,20 +86,20 @@ Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
 ## FAQ
 
-### What is object blurring with Ultralytics YOLOv8?
+### What is object blurring with Ultralytics YOLO11?
 
-Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves automatically detecting and applying a blurring effect to specific objects in images or videos. This technique enhances privacy by concealing sensitive information while retaining relevant visual data. YOLOv8's real-time processing capabilities make it suitable for applications requiring immediate privacy protection and selective focus adjustments.
+Object blurring with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves automatically detecting and applying a blurring effect to specific objects in images or videos. This technique enhances privacy by concealing sensitive information while retaining relevant visual data. YOLO11's real-time processing capabilities make it suitable for applications requiring immediate privacy protection and selective focus adjustments.
 
-### How can I implement real-time object blurring using YOLOv8?
+### How can I implement real-time object blurring using YOLO11?
 
-To implement real-time object blurring with YOLOv8, follow the provided Python example. This involves using YOLOv8 for [object detection](https://www.ultralytics.com/glossary/object-detection) and OpenCV for applying the blur effect. Here's a simplified version:
+To implement real-time object blurring with YOLO11, follow the provided Python example. This involves using YOLO11 for [object detection](https://www.ultralytics.com/glossary/object-detection) and OpenCV for applying the blur effect. Here's a simplified version:
 
 ```python
 import cv2
 
 from ultralytics import YOLO
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("path/to/video/file.mp4")
 
 while cap.isOpened():
@@ -112,7 +112,7 @@ while cap.isOpened():
         obj = im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]
         im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] = cv2.blur(obj, (50, 50))
 
-    cv2.imshow("YOLOv8 Blurring", im0)
+    cv2.imshow("YOLO11 Blurring", im0)
     if cv2.waitKey(1) & 0xFF == ord("q"):
         break
 
@@ -120,9 +120,9 @@ cap.release()
 cv2.destroyAllWindows()
 ```
 
-### What are the benefits of using Ultralytics YOLOv8 for object blurring?
+### What are the benefits of using Ultralytics YOLO11 for object blurring?
 
-Ultralytics YOLOv8 offers several advantages for object blurring:
+Ultralytics YOLO11 offers several advantages for object blurring:
 
 - **Privacy Protection**: Effectively obscure sensitive or identifiable information.
 - **Selective Focus**: Target specific objects for blurring, maintaining essential visual content.
@@ -130,10 +130,10 @@ Ultralytics YOLOv8 offers several advantages for object blurring:
 
 For more detailed applications, check the [advantages of object blurring section](#advantages-of-object-blurring).
 
-### Can I use Ultralytics YOLOv8 to blur faces in a video for privacy reasons?
+### Can I use Ultralytics YOLO11 to blur faces in a video for privacy reasons?
 
-Yes, Ultralytics YOLOv8 can be configured to detect and blur faces in videos to protect privacy. By training or using a pre-trained model to specifically recognize faces, the detection results can be processed with [OpenCV](https://www.ultralytics.com/glossary/opencv) to apply a blur effect. Refer to our guide on [object detection with YOLOv8](https://docs.ultralytics.com/models/yolov8/) and modify the code to target face detection.
+Yes, Ultralytics YOLO11 can be configured to detect and blur faces in videos to protect privacy. By training or using a pre-trained model to specifically recognize faces, the detection results can be processed with [OpenCV](https://www.ultralytics.com/glossary/opencv) to apply a blur effect. Refer to our guide on [object detection with YOLO11](https://docs.ultralytics.com/models/yolov8/) and modify the code to target face detection.
 
-### How does YOLOv8 compare to other object detection models like Faster R-CNN for object blurring?
+### How does YOLO11 compare to other object detection models like Faster R-CNN for object blurring?
 
-Ultralytics YOLOv8 typically outperforms models like Faster R-CNN in terms of speed, making it more suitable for real-time applications. While both models offer accurate detection, YOLOv8's architecture is optimized for rapid inference, which is critical for tasks like real-time object blurring. Learn more about the technical differences and performance metrics in our [YOLOv8 documentation](https://docs.ultralytics.com/models/yolov8/).
+Ultralytics YOLO11 typically outperforms models like Faster R-CNN in terms of speed, making it more suitable for real-time applications. While both models offer accurate detection, YOLO11's architecture is optimized for rapid inference, which is critical for tasks like real-time object blurring. Learn more about the technical differences and performance metrics in our [YOLO11 documentation](https://docs.ultralytics.com/models/yolov8/).

docs/en/guides/object-counting.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn to accurately identify and count objects in real-time using Ultralytics YOLOv8 for applications like crowd analysis and surveillance.
-keywords: object counting, YOLOv8, Ultralytics, real-time object detection, AI, deep learning, object tracking, crowd analysis, surveillance, resource optimization
+description: Learn to accurately identify and count objects in real-time using Ultralytics YOLO11 for applications like crowd analysis and surveillance.
+keywords: object counting, YOLO11, Ultralytics, real-time object detection, AI, deep learning, object tracking, crowd analysis, surveillance, resource optimization
 ---
 
-# Object Counting using Ultralytics YOLOv8
+# Object Counting using Ultralytics YOLO11
 
 ## What is Object Counting?
 
-Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLOv8 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) capabilities.
+Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLO11 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) capabilities.
 
 <table>
   <tr>
@@ -19,7 +19,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         allowfullscreen>
       </iframe>
       <br>
-      <strong>Watch:</strong> Object Counting using Ultralytics YOLOv8
+      <strong>Watch:</strong> Object Counting using Ultralytics YOLO11
     </td>
     <td align="center">
       <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Fj9TStNBVoY"
@@ -28,7 +28,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         allowfullscreen>
       </iframe>
       <br>
-      <strong>Watch:</strong> Class-wise Object Counting using Ultralytics YOLOv8
+      <strong>Watch:</strong> Class-wise Object Counting using Ultralytics YOLO11
     </td>
   </tr>
 </table>
@@ -43,10 +43,10 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
 |                                                                        Logistics                                                                        |                                                                         Aquaculture                                                                          |
 | :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| ![Conveyor Belt Packets Counting Using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/conveyor-belt-packets-counting.avif) | ![Fish Counting in Sea using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/fish-counting-in-sea-using-ultralytics-yolov8.avif) |
-|                                                 Conveyor Belt Packets Counting Using Ultralytics YOLOv8                                                 |                                                        Fish Counting in Sea using Ultralytics YOLOv8                                                         |
+| ![Conveyor Belt Packets Counting Using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/conveyor-belt-packets-counting.avif) | ![Fish Counting in Sea using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/fish-counting-in-sea-using-ultralytics-yolov8.avif) |
+|                                                 Conveyor Belt Packets Counting Using Ultralytics YOLO11                                                 |                                                        Fish Counting in Sea using Ultralytics YOLO11                                                         |
 
-!!! example "Object Counting using YOLOv8 Example"
+!!! example "Object Counting using YOLO11 Example"
 
     === "Count in Region"
 
@@ -55,7 +55,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -97,7 +97,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n-obb.pt")
+        model = YOLO("yolo11n-obb.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -137,7 +137,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -178,7 +178,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -219,7 +219,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -277,12 +277,12 @@ Here's a table with the `ObjectCounter` arguments:
 
 ## FAQ
 
-### How do I count objects in a video using Ultralytics YOLOv8?
+### How do I count objects in a video using Ultralytics YOLO11?
 
-To count objects in a video using Ultralytics YOLOv8, you can follow these steps:
+To count objects in a video using Ultralytics YOLO11, you can follow these steps:
 
 1. Import the necessary libraries (`cv2`, `ultralytics`).
-2. Load a pretrained YOLOv8 model.
+2. Load a pretrained YOLO11 model.
 3. Define the counting region (e.g., a polygon, line, etc.).
 4. Set up the video capture and initialize the object counter.
 5. Process each frame to track objects and count them within the defined region.
@@ -321,14 +321,14 @@ def count_objects_in_region(video_path, output_video_path, model_path):
     cv2.destroyAllWindows()
 
 
-count_objects_in_region("path/to/video.mp4", "output_video.avi", "yolov8n.pt")
+count_objects_in_region("path/to/video.mp4", "output_video.avi", "yolo11n.pt")
 ```
 
-Explore more configurations and options in the [Object Counting](#object-counting-using-ultralytics-yolov8) section.
+Explore more configurations and options in the [Object Counting](#object-counting-using-ultralytics-yolo11) section.
 
-### What are the advantages of using Ultralytics YOLOv8 for object counting?
+### What are the advantages of using Ultralytics YOLO11 for object counting?
 
-Using Ultralytics YOLOv8 for object counting offers several advantages:
+Using Ultralytics YOLO11 for object counting offers several advantages:
 
 1. **Resource Optimization:** It facilitates efficient resource management by providing accurate counts, helping optimize resource allocation in industries like inventory management.
 2. **Enhanced Security:** It enhances security and surveillance by accurately tracking and counting entities, aiding in proactive threat detection.
@@ -336,9 +336,9 @@ Using Ultralytics YOLOv8 for object counting offers several advantages:
 
 For real-world applications and code examples, visit the [Advantages of Object Counting](#advantages-of-object-counting) section.
 
-### How can I count specific classes of objects using Ultralytics YOLOv8?
+### How can I count specific classes of objects using Ultralytics YOLO11?
 
-To count specific classes of objects using Ultralytics YOLOv8, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:
+To count specific classes of objects using Ultralytics YOLO11, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:
 
 ```python
 import cv2
@@ -372,27 +372,27 @@ def count_specific_classes(video_path, output_video_path, model_path, classes_to
     cv2.destroyAllWindows()
 
 
-count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolov8n.pt", [0, 2])
+count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolo11n.pt", [0, 2])
 ```
 
 In this example, `classes_to_count=[0, 2]`, which means it counts objects of class `0` and `2` (e.g., person and car).
 
-### Why should I use YOLOv8 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models for real-time applications?
+### Why should I use YOLO11 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models for real-time applications?
 
-Ultralytics YOLOv8 provides several advantages over other object detection models like Faster R-CNN, SSD, and previous YOLO versions:
+Ultralytics YOLO11 provides several advantages over other object detection models like Faster R-CNN, SSD, and previous YOLO versions:
 
-1. **Speed and Efficiency:** YOLOv8 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and autonomous driving.
+1. **Speed and Efficiency:** YOLO11 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and autonomous driving.
 2. **[Accuracy](https://www.ultralytics.com/glossary/accuracy):** It provides state-of-the-art accuracy for object detection and tracking tasks, reducing the number of false positives and improving overall system reliability.
-3. **Ease of Integration:** YOLOv8 offers seamless integration with various platforms and devices, including mobile and edge devices, which is crucial for modern AI applications.
+3. **Ease of Integration:** YOLO11 offers seamless integration with various platforms and devices, including mobile and edge devices, which is crucial for modern AI applications.
 4. **Flexibility:** Supports various tasks like object detection, segmentation, and tracking with configurable models to meet specific use-case requirements.
 
-Check out Ultralytics [YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8/) for a deeper dive into its features and performance comparisons.
+Check out Ultralytics [YOLO11 Documentation](https://docs.ultralytics.com/models/yolo11/) for a deeper dive into its features and performance comparisons.
 
-### Can I use YOLOv8 for advanced applications like crowd analysis and traffic management?
+### Can I use YOLO11 for advanced applications like crowd analysis and traffic management?
 
-Yes, Ultralytics YOLOv8 is perfectly suited for advanced applications like crowd analysis and traffic management due to its real-time detection capabilities, scalability, and integration flexibility. Its advanced features allow for high-accuracy object tracking, counting, and classification in dynamic environments. Example use cases include:
+Yes, Ultralytics YOLO11 is perfectly suited for advanced applications like crowd analysis and traffic management due to its real-time detection capabilities, scalability, and integration flexibility. Its advanced features allow for high-accuracy object tracking, counting, and classification in dynamic environments. Example use cases include:
 
 - **Crowd Analysis:** Monitor and manage large gatherings, ensuring safety and optimizing crowd flow.
 - **Traffic Management:** Track and count vehicles, analyze traffic patterns, and manage congestion in real-time.
 
-For more information and implementation details, refer to the guide on [Real World Applications](#real-world-applications) of object counting with YOLOv8.
+For more information and implementation details, refer to the guide on [Real World Applications](#real-world-applications) of object counting with YOLO11.

docs/en/guides/object-cropping.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn how to crop and extract objects using Ultralytics YOLOv8 for focused analysis, reduced data volume, and enhanced precision.
-keywords: Ultralytics, YOLOv8, object cropping, object detection, image processing, video analysis, AI, machine learning
+description: Learn how to crop and extract objects using Ultralytics YOLO11 for focused analysis, reduced data volume, and enhanced precision.
+keywords: Ultralytics, YOLO11, object cropping, object detection, image processing, video analysis, AI, machine learning
 ---
 
-# Object Cropping using Ultralytics YOLOv8
+# Object Cropping using Ultralytics YOLO11
 
 ## What is Object Cropping?
 
-Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves isolating and extracting specific detected objects from an image or video. The YOLOv8 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation.
+Object cropping with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves isolating and extracting specific detected objects from an image or video. The YOLO11 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation.
 
 <p align="center">
   <br>
@@ -18,23 +18,23 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Object Cropping using Ultralytics YOLOv8
+  <strong>Watch:</strong> Object Cropping using Ultralytics YOLO
 </p>
 
 ## Advantages of Object Cropping?
 
-- **Focused Analysis**: YOLOv8 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene.
+- **Focused Analysis**: YOLO11 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene.
 - **Reduced Data Volume**: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks.
-- **Enhanced Precision**: YOLOv8's [object detection](https://www.ultralytics.com/glossary/object-detection) [accuracy](https://www.ultralytics.com/glossary/accuracy) ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.
+- **Enhanced Precision**: YOLO11's [object detection](https://www.ultralytics.com/glossary/object-detection) [accuracy](https://www.ultralytics.com/glossary/accuracy) ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.
 
 ## Visuals
 
 |                                                                                Airport Luggage                                                                                 |
 | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| ![Conveyor Belt at Airport Suitcases Cropping using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/suitcases-cropping-airport-conveyor-belt.avif) |
-|                                                      Suitcases Cropping at airport conveyor belt using Ultralytics YOLOv8                                                      |
+| ![Conveyor Belt at Airport Suitcases Cropping using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/suitcases-cropping-airport-conveyor-belt.avif) |
+|                                                      Suitcases Cropping at airport conveyor belt using Ultralytics YOLO11                                                      |
 
-!!! example "Object Cropping using YOLOv8 Example"
+!!! example "Object Cropping using YOLO11 Example"
 
     === "Object Cropping"
 
@@ -46,7 +46,7 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         names = model.names
 
         cap = cv2.VideoCapture("path/to/video/file.mp4")
@@ -98,22 +98,22 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
 ## FAQ
 
-### What is object cropping in Ultralytics YOLOv8 and how does it work?
+### What is object cropping in Ultralytics YOLO11 and how does it work?
 
-Object cropping using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) involves isolating and extracting specific objects from an image or video based on YOLOv8's detection capabilities. This process allows for focused analysis, reduced data volume, and enhanced [precision](https://www.ultralytics.com/glossary/precision) by leveraging YOLOv8 to identify objects with high accuracy and crop them accordingly. For an in-depth tutorial, refer to the [object cropping example](#object-cropping-using-ultralytics-yolov8).
+Object cropping using [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) involves isolating and extracting specific objects from an image or video based on YOLO11's detection capabilities. This process allows for focused analysis, reduced data volume, and enhanced [precision](https://www.ultralytics.com/glossary/precision) by leveraging YOLO11 to identify objects with high accuracy and crop them accordingly. For an in-depth tutorial, refer to the [object cropping example](#object-cropping-using-ultralytics-yolo11).
 
-### Why should I use Ultralytics YOLOv8 for object cropping over other solutions?
+### Why should I use Ultralytics YOLO11 for object cropping over other solutions?
 
-Ultralytics YOLOv8 stands out due to its precision, speed, and ease of use. It allows detailed and accurate object detection and cropping, essential for [focused analysis](#advantages-of-object-cropping) and applications needing high data integrity. Moreover, YOLOv8 integrates seamlessly with tools like OpenVINO and TensorRT for deployments requiring real-time capabilities and optimization on diverse hardware. Explore the benefits in the [guide on model export](../modes/export.md).
+Ultralytics YOLO11 stands out due to its precision, speed, and ease of use. It allows detailed and accurate object detection and cropping, essential for [focused analysis](#advantages-of-object-cropping) and applications needing high data integrity. Moreover, YOLO11 integrates seamlessly with tools like OpenVINO and TensorRT for deployments requiring real-time capabilities and optimization on diverse hardware. Explore the benefits in the [guide on model export](../modes/export.md).
 
 ### How can I reduce the data volume of my dataset using object cropping?
 
-By using Ultralytics YOLOv8 to crop only relevant objects from your images or videos, you can significantly reduce the data size, making it more efficient for storage and processing. This process involves training the model to detect specific objects and then using the results to crop and save these portions only. For more information on exploiting Ultralytics YOLOv8's capabilities, visit our [quickstart guide](../quickstart.md).
+By using Ultralytics YOLO11 to crop only relevant objects from your images or videos, you can significantly reduce the data size, making it more efficient for storage and processing. This process involves training the model to detect specific objects and then using the results to crop and save these portions only. For more information on exploiting Ultralytics YOLO11's capabilities, visit our [quickstart guide](../quickstart.md).
 
-### Can I use Ultralytics YOLOv8 for real-time video analysis and object cropping?
+### Can I use Ultralytics YOLO11 for real-time video analysis and object cropping?
 
-Yes, Ultralytics YOLOv8 can process real-time video feeds to detect and crop objects dynamically. The model's high-speed inference capabilities make it ideal for real-time applications such as surveillance, sports analysis, and automated inspection systems. Check out the [tracking and prediction modes](../modes/predict.md) to understand how to implement real-time processing.
+Yes, Ultralytics YOLO11 can process real-time video feeds to detect and crop objects dynamically. The model's high-speed inference capabilities make it ideal for real-time applications such as surveillance, sports analysis, and automated inspection systems. Check out the [tracking and prediction modes](../modes/predict.md) to understand how to implement real-time processing.
 
-### What are the hardware requirements for efficiently running YOLOv8 for object cropping?
+### What are the hardware requirements for efficiently running YOLO11 for object cropping?
 
-Ultralytics YOLOv8 is optimized for both CPU and GPU environments, but to achieve optimal performance, especially for real-time or high-volume inference, a dedicated GPU (e.g., NVIDIA Tesla, RTX series) is recommended. For deployment on lightweight devices, consider using CoreML for iOS or TFLite for Android. More details on supported devices and formats can be found in our [model deployment options](../guides/model-deployment-options.md).
+Ultralytics YOLO11 is optimized for both CPU and GPU environments, but to achieve optimal performance, especially for real-time or high-volume inference, a dedicated GPU (e.g., NVIDIA Tesla, RTX series) is recommended. For deployment on lightweight devices, consider using CoreML for iOS or TFLite for Android. More details on supported devices and formats can be found in our [model deployment options](../guides/model-deployment-options.md).

docs/en/guides/parking-management.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Optimize parking spaces and enhance safety with Ultralytics YOLOv8. Explore real-time vehicle detection and smart parking solutions.
-keywords: parking management, YOLOv8, Ultralytics, vehicle detection, real-time tracking, parking lot optimization, smart parking
+description: Optimize parking spaces and enhance safety with Ultralytics YOLO11. Explore real-time vehicle detection and smart parking solutions.
+keywords: parking management, YOLO11, Ultralytics, vehicle detection, real-time tracking, parking lot optimization, smart parking
 ---
 
-# Parking Management using Ultralytics YOLOv8 🚀
+# Parking Management using Ultralytics YOLO11 🚀
 
 ## What is Parking Management System?
 
-Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) ensures efficient and safe parking by organizing spaces and monitoring availability. YOLOv8 can improve parking lot management through real-time vehicle detection, and insights into parking occupancy.
+Parking management with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) ensures efficient and safe parking by organizing spaces and monitoring availability. YOLO11 can improve parking lot management through real-time vehicle detection, and insights into parking occupancy.
 
 <p align="center">
   <br>
@@ -18,21 +18,21 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to Implement Parking Management Using Ultralytics YOLOv8 🚀
+  <strong>Watch:</strong> How to Implement Parking Management Using Ultralytics YOLO 🚀
 </p>
 
 ## Advantages of Parking Management System?
 
 - **Efficiency**: Parking lot management optimizes the use of parking spaces and reduces congestion.
-- **Safety and Security**: Parking management using YOLOv8 improves the safety of both people and vehicles through surveillance and security measures.
-- **Reduced Emissions**: Parking management using YOLOv8 manages traffic flow to minimize idle time and emissions in parking lots.
+- **Safety and Security**: Parking management using YOLO11 improves the safety of both people and vehicles through surveillance and security measures.
+- **Reduced Emissions**: Parking management using YOLO11 manages traffic flow to minimize idle time and emissions in parking lots.
 
 ## Real World Applications
 
 |                                                                     Parking Management System                                                                      |                                                                      Parking Management System                                                                       |
 | :----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| ![Parking lots Analytics Using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/parking-management-aerial-view-ultralytics-yolov8.avif) | ![Parking management top view using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/parking-management-top-view-ultralytics-yolov8.avif) |
-|                                                      Parking management Aerial View using Ultralytics YOLOv8                                                       |                                                         Parking management Top View using Ultralytics YOLOv8                                                         |
+| ![Parking lots Analytics Using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/parking-management-aerial-view-ultralytics-yolov8.avif) | ![Parking management top view using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/parking-management-top-view-ultralytics-yolov8.avif) |
+|                                                      Parking management Aerial View using Ultralytics YOLO11                                                       |                                                         Parking management Top View using Ultralytics YOLO11                                                         |
 
 ## Parking Management System Code Workflow
 
@@ -49,7 +49,7 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr
 
     Max Image Size of 1920 * 1080 supported
 
-!!! example "Parking slots Annotator Ultralytics YOLOv8"
+!!! example "Parking slots Annotator Ultralytics YOLO11"
 
     === "Parking Annotator"
 
@@ -61,11 +61,11 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr
 
 - After defining the parking areas with polygons, click `save` to store a JSON file with the data in your working directory.
 
-![Ultralytics YOLOv8 Points Selection Demo](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-points-selection-demo.avif)
+![Ultralytics YOLO11 Points Selection Demo](https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-points-selection-demo.avif)
 
 ### Python Code for Parking Management
 
-!!! example "Parking management using YOLOv8 Example"
+!!! example "Parking management using YOLO11 Example"
 
     === "Parking Management"
 
@@ -84,7 +84,7 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr
 
         # Initialize parking management object
         parking_manager = solutions.ParkingManagement(
-            model="yolov8n.pt",  # path to model file
+            model="yolo11n.pt",  # path to model file
             json_file="bounding_boxes.json",  # path to parking annotations file
         )
 
@@ -104,7 +104,7 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr
 
 | Name                     | Type    | Default       | Description                                                    |
 | ------------------------ | ------- | ------------- | -------------------------------------------------------------- |
-| `model`                  | `str`   | `None`        | Path to the YOLOv8 model.                                      |
+| `model`                  | `str`   | `None`        | Path to the YOLO11 model.                                      |
 | `json_file`              | `str`   | `None`        | Path to the JSON file, that have all parking coordinates data. |
 | `occupied_region_color`  | `tuple` | `(0, 0, 255)` | RGB color for occupied regions.                                |
 | `available_region_color` | `tuple` | `(0, 255, 0)` | RGB color for available regions.                               |
@@ -115,33 +115,33 @@ Parking management with [Ultralytics YOLOv8](https://github.com/ultralytics/ultr
 
 ## FAQ
 
-### How does Ultralytics YOLOv8 enhance parking management systems?
+### How does Ultralytics YOLO11 enhance parking management systems?
 
-Ultralytics YOLOv8 greatly enhances parking management systems by providing **real-time vehicle detection** and monitoring. This results in optimized usage of parking spaces, reduced congestion, and improved safety through continuous surveillance. The [Parking Management System](https://github.com/ultralytics/ultralytics) enables efficient traffic flow, minimizing idle times and emissions in parking lots, thereby contributing to environmental sustainability. For further details, refer to the [parking management code workflow](#python-code-for-parking-management).
+Ultralytics YOLO11 greatly enhances parking management systems by providing **real-time vehicle detection** and monitoring. This results in optimized usage of parking spaces, reduced congestion, and improved safety through continuous surveillance. The [Parking Management System](https://github.com/ultralytics/ultralytics) enables efficient traffic flow, minimizing idle times and emissions in parking lots, thereby contributing to environmental sustainability. For further details, refer to the [parking management code workflow](#python-code-for-parking-management).
 
-### What are the benefits of using Ultralytics YOLOv8 for smart parking?
+### What are the benefits of using Ultralytics YOLO11 for smart parking?
 
-Using Ultralytics YOLOv8 for smart parking yields numerous benefits:
+Using Ultralytics YOLO11 for smart parking yields numerous benefits:
 
 - **Efficiency**: Optimizes the use of parking spaces and decreases congestion.
 - **Safety and Security**: Enhances surveillance and ensures the safety of vehicles and pedestrians.
 - **Environmental Impact**: Helps in reducing emissions by minimizing vehicle idle times. More details on the advantages can be seen [here](#advantages-of-parking-management-system).
 
-### How can I define parking spaces using Ultralytics YOLOv8?
+### How can I define parking spaces using Ultralytics YOLO11?
 
-Defining parking spaces is straightforward with Ultralytics YOLOv8:
+Defining parking spaces is straightforward with Ultralytics YOLO11:
 
 1. Capture a frame from a video or camera stream.
 2. Use the provided code to launch a GUI for selecting an image and drawing polygons to define parking spaces.
 3. Save the labeled data in JSON format for further processing. For comprehensive instructions, check the [selection of points](#selection-of-points) section.
 
-### Can I customize the YOLOv8 model for specific parking management needs?
+### Can I customize the YOLO11 model for specific parking management needs?
 
-Yes, Ultralytics YOLOv8 allows customization for specific parking management needs. You can adjust parameters such as the **occupied and available region colors**, margins for text display, and much more. Utilizing the `ParkingManagement` class's [optional arguments](#optional-arguments-parkingmanagement), you can tailor the model to suit your particular requirements, ensuring maximum efficiency and effectiveness.
+Yes, Ultralytics YOLO11 allows customization for specific parking management needs. You can adjust parameters such as the **occupied and available region colors**, margins for text display, and much more. Utilizing the `ParkingManagement` class's [optional arguments](#optional-arguments-parkingmanagement), you can tailor the model to suit your particular requirements, ensuring maximum efficiency and effectiveness.
 
-### What are some real-world applications of Ultralytics YOLOv8 in parking lot management?
+### What are some real-world applications of Ultralytics YOLO11 in parking lot management?
 
-Ultralytics YOLOv8 is utilized in various real-world applications for parking lot management, including:
+Ultralytics YOLO11 is utilized in various real-world applications for parking lot management, including:
 
 - **Parking Space Detection**: Accurately identifying available and occupied spaces.
 - **Surveillance**: Enhancing security through real-time monitoring.

docs/en/guides/preprocessing_annotated_data.md

@@ -1,7 +1,7 @@
 ---
 comments: true
 description: Learn essential data preprocessing techniques for annotated computer vision data, including resizing, normalizing, augmenting, and splitting datasets for optimal model training.
-keywords: data preprocessing, computer vision, image resizing, normalization, data augmentation, training dataset, validation dataset, test dataset, YOLOv8
+keywords: data preprocessing, computer vision, image resizing, normalization, data augmentation, training dataset, validation dataset, test dataset, YOLO11
 ---
 
 # Data Preprocessing Techniques for Annotated [Computer Vision](https://www.ultralytics.com/glossary/computer-vision-cv) Data
@@ -36,7 +36,7 @@ To make resizing a simpler task, you can use the following tools:
 - **[OpenCV](https://www.ultralytics.com/glossary/opencv)**: A popular computer vision library with extensive functions for image processing.
 - **PIL (Pillow)**: A Python Imaging Library for opening, manipulating, and saving image files.
 
-With respect to YOLOv8, the 'imgsz' parameter during [model training](../modes/train.md) allows for flexible input sizes. When set to a specific size, such as 640, the model will resize input images so their largest dimension is 640 pixels while maintaining the original aspect ratio.
+With respect to YOLO11, the 'imgsz' parameter during [model training](../modes/train.md) allows for flexible input sizes. When set to a specific size, such as 640, the model will resize input images so their largest dimension is 640 pixels while maintaining the original aspect ratio.
 
 By evaluating your model's and dataset's specific needs, you can determine whether resizing is a necessary preprocessing step or if your model can efficiently handle images of varying sizes.
 
@@ -47,7 +47,7 @@ Another preprocessing technique is normalization. Normalization scales the pixel
 - **Min-Max Scaling**: Scales pixel values to a range of 0 to 1.
 - **Z-Score Normalization**: Scales pixel values based on their mean and standard deviation.
 
-With respect to YOLOv8, normalization is seamlessly handled as part of its preprocessing pipeline during model training. YOLOv8 automatically performs several preprocessing steps, including conversion to RGB, scaling pixel values to the range [0, 1], and normalization using predefined mean and standard deviation values.
+With respect to YOLO11, normalization is seamlessly handled as part of its preprocessing pipeline during model training. YOLO11 automatically performs several preprocessing steps, including conversion to RGB, scaling pixel values to the range [0, 1], and normalization using predefined mean and standard deviation values.
 
 ### Splitting the Dataset
 
@@ -76,9 +76,9 @@ Common augmentation techniques include flipping, rotation, scaling, and color ad
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/overview-of-data-augmentations.avif" alt="Overview of Data Augmentations">
 </p>
 
-With respect to YOLOv8, you can [augment your custom dataset](../modes/train.md) by modifying the dataset configuration file, a .yaml file. In this file, you can add an augmentation section with parameters that specify how you want to augment your data.
+With respect to YOLO11, you can [augment your custom dataset](../modes/train.md) by modifying the dataset configuration file, a .yaml file. In this file, you can add an augmentation section with parameters that specify how you want to augment your data.
 
-The [Ultralytics YOLOv8 repository](https://github.com/ultralytics/ultralytics/tree/main) supports a wide range of data augmentations. You can apply various transformations such as:
+The [Ultralytics YOLO11 repository](https://github.com/ultralytics/ultralytics/tree/main) supports a wide range of data augmentations. You can apply various transformations such as:
 
 - Random Crops
 - Flipping: Images can be flipped horizontally or vertically.
@@ -89,12 +89,12 @@ Also, you can adjust the intensity of these augmentation techniques through spec
 
 ## A Case Study of Preprocessing
 
-Consider a project aimed at developing a model to detect and classify different types of vehicles in traffic images using YOLOv8. We've collected traffic images and annotated them with bounding boxes and labels.
+Consider a project aimed at developing a model to detect and classify different types of vehicles in traffic images using YOLO11. We've collected traffic images and annotated them with bounding boxes and labels.
 
 Here's what each step of preprocessing would look like for this project:
 
-- Resizing Images: Since YOLOv8 handles flexible input sizes and performs resizing automatically, manual resizing is not required. The model will adjust the image size according to the specified 'imgsz' parameter during training.
-- Normalizing Pixel Values: YOLOv8 automatically normalizes pixel values to a range of 0 to 1 during preprocessing, so it's not required.
+- Resizing Images: Since YOLO11 handles flexible input sizes and performs resizing automatically, manual resizing is not required. The model will adjust the image size according to the specified 'imgsz' parameter during training.
+- Normalizing Pixel Values: YOLO11 automatically normalizes pixel values to a range of 0 to 1 during preprocessing, so it's not required.
 - Splitting the Dataset: Divide the dataset into training (70%), validation (20%), and test (10%) sets using tools like scikit-learn.
 - [Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation): Modify the dataset configuration file (.yaml) to include data augmentation techniques such as random crops, horizontal flips, and brightness adjustments.
 
@@ -132,12 +132,12 @@ Having discussions about your project with other computer vision enthusiasts can
 
 ### Channels to Connect with the Community
 
-- **GitHub Issues:** Visit the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
+- **GitHub Issues:** Visit the YOLO11 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
 - **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Refer to the [official YOLOv8 documentation](./index.md) for thorough guides and valuable insights on numerous computer vision tasks and projects.
+- **Ultralytics YOLO11 Documentation:** Refer to the [official YOLO11 documentation](./index.md) for thorough guides and valuable insights on numerous computer vision tasks and projects.
 
 ## Your Dataset Is Ready!
 
@@ -151,7 +151,7 @@ Data preprocessing is essential in computer vision projects because it ensures t
 
 ### How can I use Ultralytics YOLO for data augmentation?
 
-For data augmentation with Ultralytics YOLOv8, you need to modify the dataset configuration file (.yaml). In this file, you can specify various augmentation techniques such as random crops, horizontal flips, and brightness adjustments. This can be effectively done using the training configurations [explained here](../modes/train.md). Data augmentation helps create a more robust dataset, reduce [overfitting](https://www.ultralytics.com/glossary/overfitting), and improve model generalization.
+For data augmentation with Ultralytics YOLO11, you need to modify the dataset configuration file (.yaml). In this file, you can specify various augmentation techniques such as random crops, horizontal flips, and brightness adjustments. This can be effectively done using the training configurations [explained here](../modes/train.md). Data augmentation helps create a more robust dataset, reduce [overfitting](https://www.ultralytics.com/glossary/overfitting), and improve model generalization.
 
 ### What are the best data normalization techniques for computer vision data?
 
@@ -160,12 +160,12 @@ Normalization scales pixel values to a standard range for faster convergence and
 - **Min-Max Scaling**: Scales pixel values to a range of 0 to 1.
 - **Z-Score Normalization**: Scales pixel values based on their mean and standard deviation.
 
-For YOLOv8, normalization is handled automatically, including conversion to RGB and pixel value scaling. Learn more about it in the [model training section](../modes/train.md).
+For YOLO11, normalization is handled automatically, including conversion to RGB and pixel value scaling. Learn more about it in the [model training section](../modes/train.md).
 
 ### How should I split my annotated dataset for training?
 
 To split your dataset, a common practice is to divide it into 70% for training, 20% for validation, and 10% for testing. It is important to maintain the data distribution of classes across these splits and avoid data leakage by performing augmentation only on the training set. Use tools like scikit-learn or [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) for efficient dataset splitting. See the detailed guide on [dataset preparation](../guides/data-collection-and-annotation.md).
 
-### Can I handle varying image sizes in YOLOv8 without manual resizing?
+### Can I handle varying image sizes in YOLO11 without manual resizing?
 
-Yes, Ultralytics YOLOv8 can handle varying image sizes through the 'imgsz' parameter during model training. This parameter ensures that images are resized so their largest dimension matches the specified size (e.g., 640 pixels), while maintaining the aspect ratio. For more flexible input handling and automatic adjustments, check the [model training section](../modes/train.md).
+Yes, Ultralytics YOLO11 can handle varying image sizes through the 'imgsz' parameter during model training. This parameter ensures that images are resized so their largest dimension matches the specified size (e.g., 640 pixels), while maintaining the aspect ratio. For more flexible input handling and automatic adjustments, check the [model training section](../modes/train.md).

docs/en/guides/queue-management.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn how to manage and optimize queues using Ultralytics YOLOv8 to reduce wait times and increase efficiency in various real-world applications.
-keywords: queue management, YOLOv8, Ultralytics, reduce wait times, efficiency, customer satisfaction, retail, airports, healthcare, banks
+description: Learn how to manage and optimize queues using Ultralytics YOLO11 to reduce wait times and increase efficiency in various real-world applications.
+keywords: queue management, YOLO11, Ultralytics, reduce wait times, efficiency, customer satisfaction, retail, airports, healthcare, banks
 ---
 
-# Queue Management using Ultralytics YOLOv8 🚀
+# Queue Management using Ultralytics YOLO11 🚀
 
 ## What is Queue Management?
 
-Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves organizing and controlling lines of people or vehicles to reduce wait times and enhance efficiency. It's about optimizing queues to improve customer satisfaction and system performance in various settings like retail, banks, airports, and healthcare facilities.
+Queue management using [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves organizing and controlling lines of people or vehicles to reduce wait times and enhance efficiency. It's about optimizing queues to improve customer satisfaction and system performance in various settings like retail, banks, airports, and healthcare facilities.
 
 <p align="center">
   <br>
@@ -18,7 +18,7 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to Implement Queue Management with Ultralytics YOLOv8 | Airport and Metro Station
+  <strong>Watch:</strong> How to Implement Queue Management with Ultralytics YOLO11 | Airport and Metro Station
 </p>
 
 ## Advantages of Queue Management?
@@ -30,10 +30,10 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
 
 |                                                                                            Logistics                                                                                            |                                                                            Retail                                                                             |
 | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| ![Queue management at airport ticket counter using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/queue-management-airport-ticket-counter-ultralytics-yolov8.avif) | ![Queue monitoring in crowd using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/queue-monitoring-crowd-ultralytics-yolov8.avif) |
-|                                                               Queue management at airport ticket counter Using Ultralytics YOLOv8                                                               |                                                         Queue monitoring in crowd Ultralytics YOLOv8                                                          |
+| ![Queue management at airport ticket counter using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/queue-management-airport-ticket-counter-ultralytics-yolov8.avif) | ![Queue monitoring in crowd using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/queue-monitoring-crowd-ultralytics-yolov8.avif) |
+|                                                               Queue management at airport ticket counter Using Ultralytics YOLO11                                                               |                                                         Queue monitoring in crowd Ultralytics YOLO11                                                          |
 
-!!! example "Queue Management using YOLOv8 Example"
+!!! example "Queue Management using YOLO11 Example"
 
     === "Queue Manager"
 
@@ -42,7 +42,7 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
 
         assert cap.isOpened(), "Error reading video file"
@@ -84,7 +84,7 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
 
         assert cap.isOpened(), "Error reading video file"
@@ -135,11 +135,11 @@ Queue management using [Ultralytics YOLOv8](https://github.com/ultralytics/ultra
 
 ## FAQ
 
-### How can I use Ultralytics YOLOv8 for real-time queue management?
+### How can I use Ultralytics YOLO11 for real-time queue management?
 
-To use Ultralytics YOLOv8 for real-time queue management, you can follow these steps:
+To use Ultralytics YOLO11 for real-time queue management, you can follow these steps:
 
-1. Load the YOLOv8 model with `YOLO("yolov8n.pt")`.
+1. Load the YOLO11 model with `YOLO("yolo11n.pt")`.
 2. Capture the video feed using `cv2.VideoCapture`.
 3. Define the region of interest (ROI) for queue management.
 4. Process frames to detect objects and manage queues.
@@ -151,7 +151,7 @@ import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("path/to/video.mp4")
 queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
 
@@ -176,9 +176,9 @@ cv2.destroyAllWindows()
 
 Leveraging Ultralytics [HUB](https://docs.ultralytics.com/hub/) can streamline this process by providing a user-friendly platform for deploying and managing your queue management solution.
 
-### What are the key advantages of using Ultralytics YOLOv8 for queue management?
+### What are the key advantages of using Ultralytics YOLO11 for queue management?
 
-Using Ultralytics YOLOv8 for queue management offers several benefits:
+Using Ultralytics YOLO11 for queue management offers several benefits:
 
 - **Plummeting Waiting Times:** Efficiently organizes queues, reducing customer wait times and boosting satisfaction.
 - **Enhancing Efficiency:** Analyzes queue data to optimize staff deployment and operations, thereby reducing costs.
@@ -187,20 +187,20 @@ Using Ultralytics YOLOv8 for queue management offers several benefits:
 
 For more details, explore our [Queue Management](https://docs.ultralytics.com/reference/solutions/queue_management/) solutions.
 
-### Why should I choose Ultralytics YOLOv8 over competitors like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) or Detectron2 for queue management?
+### Why should I choose Ultralytics YOLO11 over competitors like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) or Detectron2 for queue management?
 
-Ultralytics YOLOv8 has several advantages over TensorFlow and Detectron2 for queue management:
+Ultralytics YOLO11 has several advantages over TensorFlow and Detectron2 for queue management:
 
-- **Real-time Performance:** YOLOv8 is known for its real-time detection capabilities, offering faster processing speeds.
+- **Real-time Performance:** YOLO11 is known for its real-time detection capabilities, offering faster processing speeds.
 - **Ease of Use:** Ultralytics provides a user-friendly experience, from training to deployment, via [Ultralytics HUB](https://docs.ultralytics.com/hub/).
 - **Pretrained Models:** Access to a range of pretrained models, minimizing the time needed for setup.
 - **Community Support:** Extensive documentation and active community support make problem-solving easier.
 
 Learn how to get started with [Ultralytics YOLO](https://docs.ultralytics.com/quickstart/).
 
-### Can Ultralytics YOLOv8 handle multiple types of queues, such as in airports and retail?
+### Can Ultralytics YOLO11 handle multiple types of queues, such as in airports and retail?
 
-Yes, Ultralytics YOLOv8 can manage various types of queues, including those in airports and retail environments. By configuring the QueueManager with specific regions and settings, YOLOv8 can adapt to different queue layouts and densities.
+Yes, Ultralytics YOLO11 can manage various types of queues, including those in airports and retail environments. By configuring the QueueManager with specific regions and settings, YOLO11 can adapt to different queue layouts and densities.
 
 Example for airports:
 
@@ -215,9 +215,9 @@ queue_airport = solutions.QueueManager(
 
 For more information on diverse applications, check out our [Real World Applications](#real-world-applications) section.
 
-### What are some real-world applications of Ultralytics YOLOv8 in queue management?
+### What are some real-world applications of Ultralytics YOLO11 in queue management?
 
-Ultralytics YOLOv8 is used in various real-world applications for queue management:
+Ultralytics YOLO11 is used in various real-world applications for queue management:
 
 - **Retail:** Monitors checkout lines to reduce wait times and improve customer satisfaction.
 - **Airports:** Manages queues at ticket counters and security checkpoints for a smoother passenger experience.

docs/en/guides/sahi-tiled-inference.md

@@ -1,12 +1,12 @@
 ---
 comments: true
-description: Learn how to implement YOLOv8 with SAHI for sliced inference. Optimize memory usage and enhance detection accuracy for large-scale applications.
-keywords: YOLOv8, SAHI, Sliced Inference, Object Detection, Ultralytics, High-resolution Images, Computational Efficiency, Integration Guide
+description: Learn how to implement YOLO11 with SAHI for sliced inference. Optimize memory usage and enhance detection accuracy for large-scale applications.
+keywords: YOLO11, SAHI, Sliced Inference, Object Detection, Ultralytics, High-resolution Images, Computational Efficiency, Integration Guide
 ---
 
-# Ultralytics Docs: Using YOLOv8 with SAHI for Sliced Inference
+# Ultralytics Docs: Using YOLO11 with SAHI for Sliced Inference
 
-Welcome to the Ultralytics documentation on how to use YOLOv8 with [SAHI](https://github.com/obss/sahi) (Slicing Aided Hyper Inference). This comprehensive guide aims to furnish you with all the essential knowledge you'll need to implement SAHI alongside YOLOv8. We'll deep-dive into what SAHI is, why sliced inference is critical for large-scale applications, and how to integrate these functionalities with YOLOv8 for enhanced [object detection](https://www.ultralytics.com/glossary/object-detection) performance.
+Welcome to the Ultralytics documentation on how to use YOLO11 with [SAHI](https://github.com/obss/sahi) (Slicing Aided Hyper Inference). This comprehensive guide aims to furnish you with all the essential knowledge you'll need to implement SAHI alongside YOLO11. We'll deep-dive into what SAHI is, why sliced inference is critical for large-scale applications, and how to integrate these functionalities with YOLO11 for enhanced [object detection](https://www.ultralytics.com/glossary/object-detection) performance.
 
 <p align="center">
   <img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/sahi-sliced-inference-overview.avif" alt="SAHI Sliced Inference Overview">
@@ -24,7 +24,7 @@ SAHI (Slicing Aided Hyper Inference) is an innovative library designed to optimi
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Inference with SAHI (Slicing Aided Hyper Inference) using Ultralytics YOLOv8
+  <strong>Watch:</strong> Inference with SAHI (Slicing Aided Hyper Inference) using Ultralytics YOLO11
 </p>
 
 ### Key Features of SAHI
@@ -47,12 +47,12 @@ Sliced Inference refers to the practice of subdividing a large or high-resolutio
 
 <table border="0">
   <tr>
-    <th>YOLOv8 without SAHI</th>
-    <th>YOLOv8 with SAHI</th>
+    <th>YOLO11 without SAHI</th>
+    <th>YOLO11 with SAHI</th>
   </tr>
   <tr>
-    <td><img src="https://github.com/ultralytics/docs/releases/download/0/yolov8-without-sahi.avif" alt="YOLOv8 without SAHI" width="640"></td>
-    <td><img src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-sahi.avif" alt="YOLOv8 with SAHI" width="640"></td>
+    <td><img src="https://github.com/ultralytics/docs/releases/download/0/yolov8-without-sahi.avif" alt="YOLO11 without SAHI" width="640"></td>
+    <td><img src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-sahi.avif" alt="YOLO11 with SAHI" width="640"></td>
   </tr>
 </table>
 
@@ -68,15 +68,15 @@ pip install -U ultralytics sahi
 
 ### Import Modules and Download Resources
 
-Here's how to import the necessary modules and download a YOLOv8 model and some test images:
+Here's how to import the necessary modules and download a YOLO11 model and some test images:
 
 ```python
 from sahi.utils.file import download_from_url
 from sahi.utils.yolov8 import download_yolov8s_model
 
-# Download YOLOv8 model
-yolov8_model_path = "models/yolov8s.pt"
-download_yolov8s_model(yolov8_model_path)
+# Download YOLO11 model
+model_path = "models/yolo11s.pt"
+download_yolov8s_model(model_path)
 
 # Download test images
 download_from_url(
@@ -89,11 +89,11 @@ download_from_url(
 )
 ```
 
-## Standard Inference with YOLOv8
+## Standard Inference with YOLO11
 
 ### Instantiate the Model
 
-You can instantiate a YOLOv8 model for object detection like this:
+You can instantiate a YOLO11 model for object detection like this:
 
 ```python
 from sahi import AutoDetectionModel
@@ -129,7 +129,7 @@ result.export_visuals(export_dir="demo_data/")
 Image("demo_data/prediction_visual.png")
 ```
 
-## Sliced Inference with YOLOv8
+## Sliced Inference with YOLO11
 
 Perform sliced inference by specifying the slice dimensions and overlap ratios:
 
@@ -170,7 +170,7 @@ from sahi.predict import predict
 
 predict(
     model_type="yolov8",
-    model_path="path/to/yolov8n.pt",
+    model_path="path/to/yolo11n.pt",
     model_device="cpu",  # or 'cuda:0'
     model_confidence_threshold=0.4,
     source="path/to/dir",
@@ -181,7 +181,7 @@ predict(
 )
 ```
 
-That's it! Now you're equipped to use YOLOv8 with SAHI for both standard and sliced inference.
+That's it! Now you're equipped to use YOLO11 with SAHI for both standard and sliced inference.
 
 ## Citations and Acknowledgments
 
@@ -206,23 +206,23 @@ We extend our thanks to the SAHI research group for creating and maintaining thi
 
 ## FAQ
 
-### How can I integrate YOLOv8 with SAHI for sliced inference in object detection?
+### How can I integrate YOLO11 with SAHI for sliced inference in object detection?
 
-Integrating Ultralytics YOLOv8 with SAHI (Slicing Aided Hyper Inference) for sliced inference optimizes your object detection tasks on high-resolution images by partitioning them into manageable slices. This approach improves memory usage and ensures high detection accuracy. To get started, you need to install the ultralytics and sahi libraries:
+Integrating Ultralytics YOLO11 with SAHI (Slicing Aided Hyper Inference) for sliced inference optimizes your object detection tasks on high-resolution images by partitioning them into manageable slices. This approach improves memory usage and ensures high detection accuracy. To get started, you need to install the ultralytics and sahi libraries:
 
 ```bash
 pip install -U ultralytics sahi
 ```
 
-Then, download a YOLOv8 model and test images:
+Then, download a YOLO11 model and test images:
 
 ```python
 from sahi.utils.file import download_from_url
 from sahi.utils.yolov8 import download_yolov8s_model
 
-# Download YOLOv8 model
-yolov8_model_path = "models/yolov8s.pt"
-download_yolov8s_model(yolov8_model_path)
+# Download YOLO11 model
+model_path = "models/yolo11s.pt"
+download_yolov8s_model(model_path)
 
 # Download test images
 download_from_url(
@@ -231,11 +231,11 @@ download_from_url(
 )
 ```
 
-For more detailed instructions, refer to our [Sliced Inference guide](#sliced-inference-with-yolov8).
+For more detailed instructions, refer to our [Sliced Inference guide](#sliced-inference-with-yolo11).
 
-### Why should I use SAHI with YOLOv8 for object detection on large images?
+### Why should I use SAHI with YOLO11 for object detection on large images?
 
-Using SAHI with Ultralytics YOLOv8 for object detection on large images offers several benefits:
+Using SAHI with Ultralytics YOLO11 for object detection on large images offers several benefits:
 
 - **Reduced Computational Burden**: Smaller slices are faster to process and consume less memory, making it feasible to run high-quality detections on hardware with limited resources.
 - **Maintained Detection Accuracy**: SAHI uses intelligent algorithms to merge overlapping boxes, preserving the detection quality.
@@ -243,9 +243,9 @@ Using SAHI with Ultralytics YOLOv8 for object detection on large images offers s
 
 Learn more about the [benefits of sliced inference](#benefits-of-sliced-inference) in our documentation.
 
-### Can I visualize prediction results when using YOLOv8 with SAHI?
+### Can I visualize prediction results when using YOLO11 with SAHI?
 
-Yes, you can visualize prediction results when using YOLOv8 with SAHI. Here's how you can export and visualize the results:
+Yes, you can visualize prediction results when using YOLO11 with SAHI. Here's how you can export and visualize the results:
 
 ```python
 from IPython.display import Image
@@ -256,9 +256,9 @@ Image("demo_data/prediction_visual.png")
 
 This command will save the visualized predictions to the specified directory and you can then load the image to view it in your notebook or application. For a detailed guide, check out the [Standard Inference section](#visualize-results).
 
-### What features does SAHI offer for improving YOLOv8 object detection?
+### What features does SAHI offer for improving YOLO11 object detection?
 
-SAHI (Slicing Aided Hyper Inference) offers several features that complement Ultralytics YOLOv8 for object detection:
+SAHI (Slicing Aided Hyper Inference) offers several features that complement Ultralytics YOLO11 for object detection:
 
 - **Seamless Integration**: SAHI easily integrates with YOLO models, requiring minimal code adjustments.
 - **Resource Efficiency**: It partitions large images into smaller slices, which optimizes memory usage and speed.
@@ -266,9 +266,9 @@ SAHI (Slicing Aided Hyper Inference) offers several features that complement Ult
 
 For a deeper understanding, read about SAHI's [key features](#key-features-of-sahi).
 
-### How do I handle large-scale inference projects using YOLOv8 and SAHI?
+### How do I handle large-scale inference projects using YOLO11 and SAHI?
 
-To handle large-scale inference projects using YOLOv8 and SAHI, follow these best practices:
+To handle large-scale inference projects using YOLO11 and SAHI, follow these best practices:
 
 1. **Install Required Libraries**: Ensure that you have the latest versions of ultralytics and sahi.
 2. **Configure Sliced Inference**: Determine the optimal slice dimensions and overlap ratios for your specific project.
@@ -281,7 +281,7 @@ from sahi.predict import predict
 
 predict(
     model_type="yolov8",
-    model_path="path/to/yolov8n.pt",
+    model_path="path/to/yolo11n.pt",
     model_device="cpu",  # or 'cuda:0'
     model_confidence_threshold=0.4,
     source="path/to/dir",

docs/en/guides/security-alarm-system.md

@@ -1,17 +1,17 @@
 ---
 comments: true
-description: Enhance your security with real-time object detection using Ultralytics YOLOv8. Reduce false positives and integrate seamlessly with existing systems.
-keywords: YOLOv8, Security Alarm System, real-time object detection, Ultralytics, computer vision, integration, false positives
+description: Enhance your security with real-time object detection using Ultralytics YOLO11. Reduce false positives and integrate seamlessly with existing systems.
+keywords: YOLO11, Security Alarm System, real-time object detection, Ultralytics, computer vision, integration, false positives
 ---
 
-# Security Alarm System Project Using Ultralytics YOLOv8
+# Security Alarm System Project Using Ultralytics YOLO11
 
 <img src="https://github.com/ultralytics/docs/releases/download/0/security-alarm-system-ultralytics-yolov8.avif" alt="Security Alarm System">
 
-The Security Alarm System Project utilizing Ultralytics YOLOv8 integrates advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) capabilities to enhance security measures. YOLOv8, developed by Ultralytics, provides real-time object detection, allowing the system to identify and respond to potential security threats promptly. This project offers several advantages:
+The Security Alarm System Project utilizing Ultralytics YOLO11 integrates advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) capabilities to enhance security measures. YOLO11, developed by Ultralytics, provides real-time object detection, allowing the system to identify and respond to potential security threats promptly. This project offers several advantages:
 
-- **Real-time Detection:** YOLOv8's efficiency enables the Security Alarm System to detect and respond to security incidents in real-time, minimizing response time.
-- **[Accuracy](https://www.ultralytics.com/glossary/accuracy):** YOLOv8 is known for its accuracy in object detection, reducing false positives and enhancing the reliability of the security alarm system.
+- **Real-time Detection:** YOLO11's efficiency enables the Security Alarm System to detect and respond to security incidents in real-time, minimizing response time.
+- **[Accuracy](https://www.ultralytics.com/glossary/accuracy):** YOLO11 is known for its accuracy in object detection, reducing false positives and enhancing the reliability of the security alarm system.
 - **Integration Capabilities:** The project can be seamlessly integrated with existing security infrastructure, providing an upgraded layer of intelligent surveillance.
 
 <p align="center">
@@ -22,7 +22,7 @@ The Security Alarm System Project utilizing Ultralytics YOLOv8 integrates advanc
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Security Alarm System Project with Ultralytics YOLOv8 <a href="https://www.ultralytics.com/glossary/object-detection">Object Detection</a>
+  <strong>Watch:</strong> Security Alarm System Project with Ultralytics YOLO11 <a href="https://www.ultralytics.com/glossary/object-detection">Object Detection</a>
 </p>
 
 ### Code
@@ -90,7 +90,7 @@ class ObjectDetection:
         self.email_sent = False
 
         # model information
-        self.model = YOLO("yolov8n.pt")
+        self.model = YOLO("yolo11n.pt")
 
         # visual information
         self.annotator = None
@@ -155,7 +155,7 @@ class ObjectDetection:
                 self.email_sent = False
 
             self.display_fps(im0)
-            cv2.imshow("YOLOv8 Detection", im0)
+            cv2.imshow("YOLO11 Detection", im0)
             frame_count += 1
             if cv2.waitKey(5) & 0xFF == 27:
                 break
@@ -179,22 +179,22 @@ That's it! When you execute the code, you'll receive a single notification on yo
 
 ## FAQ
 
-### How does Ultralytics YOLOv8 improve the accuracy of a security alarm system?
+### How does Ultralytics YOLO11 improve the accuracy of a security alarm system?
 
-Ultralytics YOLOv8 enhances security alarm systems by delivering high-accuracy, real-time object detection. Its advanced algorithms significantly reduce false positives, ensuring that the system only responds to genuine threats. This increased reliability can be seamlessly integrated with existing security infrastructure, upgrading the overall surveillance quality.
+Ultralytics YOLO11 enhances security alarm systems by delivering high-accuracy, real-time object detection. Its advanced algorithms significantly reduce false positives, ensuring that the system only responds to genuine threats. This increased reliability can be seamlessly integrated with existing security infrastructure, upgrading the overall surveillance quality.
 
-### Can I integrate Ultralytics YOLOv8 with my existing security infrastructure?
+### Can I integrate Ultralytics YOLO11 with my existing security infrastructure?
 
-Yes, Ultralytics YOLOv8 can be seamlessly integrated with your existing security infrastructure. The system supports various modes and provides flexibility for customization, allowing you to enhance your existing setup with advanced object detection capabilities. For detailed instructions on integrating YOLOv8 in your projects, visit the [integration section](https://docs.ultralytics.com/integrations/).
+Yes, Ultralytics YOLO11 can be seamlessly integrated with your existing security infrastructure. The system supports various modes and provides flexibility for customization, allowing you to enhance your existing setup with advanced object detection capabilities. For detailed instructions on integrating YOLO11 in your projects, visit the [integration section](https://docs.ultralytics.com/integrations/).
 
-### What are the storage requirements for running Ultralytics YOLOv8?
+### What are the storage requirements for running Ultralytics YOLO11?
 
-Running Ultralytics YOLOv8 on a standard setup typically requires around 5GB of free disk space. This includes space for storing the YOLOv8 model and any additional dependencies. For cloud-based solutions, Ultralytics HUB offers efficient project management and dataset handling, which can optimize storage needs. Learn more about the [Pro Plan](../hub/pro.md) for enhanced features including extended storage.
+Running Ultralytics YOLO11 on a standard setup typically requires around 5GB of free disk space. This includes space for storing the YOLO11 model and any additional dependencies. For cloud-based solutions, Ultralytics HUB offers efficient project management and dataset handling, which can optimize storage needs. Learn more about the [Pro Plan](../hub/pro.md) for enhanced features including extended storage.
 
-### What makes Ultralytics YOLOv8 different from other object detection models like Faster R-CNN or SSD?
+### What makes Ultralytics YOLO11 different from other object detection models like Faster R-CNN or SSD?
 
-Ultralytics YOLOv8 provides an edge over models like Faster R-CNN or SSD with its real-time detection capabilities and higher accuracy. Its unique architecture allows it to process images much faster without compromising on [precision](https://www.ultralytics.com/glossary/precision), making it ideal for time-sensitive applications like security alarm systems. For a comprehensive comparison of object detection models, you can explore our [guide](https://docs.ultralytics.com/models/).
+Ultralytics YOLO11 provides an edge over models like Faster R-CNN or SSD with its real-time detection capabilities and higher accuracy. Its unique architecture allows it to process images much faster without compromising on [precision](https://www.ultralytics.com/glossary/precision), making it ideal for time-sensitive applications like security alarm systems. For a comprehensive comparison of object detection models, you can explore our [guide](https://docs.ultralytics.com/models/).
 
-### How can I reduce the frequency of false positives in my security system using Ultralytics YOLOv8?
+### How can I reduce the frequency of false positives in my security system using Ultralytics YOLO11?
 
-To reduce false positives, ensure your Ultralytics YOLOv8 model is adequately trained with a diverse and well-annotated dataset. Fine-tuning hyperparameters and regularly updating the model with new data can significantly improve detection accuracy. Detailed [hyperparameter tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning) techniques can be found in our [hyperparameter tuning guide](../guides/hyperparameter-tuning.md).
+To reduce false positives, ensure your Ultralytics YOLO11 model is adequately trained with a diverse and well-annotated dataset. Fine-tuning hyperparameters and regularly updating the model with new data can significantly improve detection accuracy. Detailed [hyperparameter tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning) techniques can be found in our [hyperparameter tuning guide](../guides/hyperparameter-tuning.md).

docs/en/guides/speed-estimation.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn how to estimate object speed using Ultralytics YOLOv8 for applications in traffic control, autonomous navigation, and surveillance.
-keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision, traffic control, autonomous navigation, surveillance, security
+description: Learn how to estimate object speed using Ultralytics YOLO11 for applications in traffic control, autonomous navigation, and surveillance.
+keywords: Ultralytics YOLO11, speed estimation, object tracking, computer vision, traffic control, autonomous navigation, surveillance, security
 ---
 
-# Speed Estimation using Ultralytics YOLOv8 🚀
+# Speed Estimation using Ultralytics YOLO11 🚀
 
 ## What is Speed Estimation?
 
-[Speed estimation](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) is the process of calculating the rate of movement of an object within a given context, often employed in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications. Using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](../modes/track.md) alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.
+[Speed estimation](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) is the process of calculating the rate of movement of an object within a given context, often employed in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications. Using [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](../modes/track.md) alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.
 
 <p align="center">
   <br>
@@ -18,12 +18,12 @@ keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Speed Estimation using Ultralytics YOLOv8
+  <strong>Watch:</strong> Speed Estimation using Ultralytics YOLO11
 </p>
 
 !!! tip "Check Out Our Blog"
 
-    For deeper insights into speed estimation, check out our blog post: [Ultralytics YOLOv8 for Speed Estimation in Computer Vision Projects](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects)
+    For deeper insights into speed estimation, check out our blog post: [Ultralytics YOLO11 for Speed Estimation in Computer Vision Projects](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects)
 
 ## Advantages of Speed Estimation?
 
@@ -35,10 +35,10 @@ keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision
 
 |                                                                            Transportation                                                                            |                                                                              Transportation                                                                              |
 | :------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| ![Speed Estimation on Road using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/speed-estimation-on-road-using-ultralytics-yolov8.avif) | ![Speed Estimation on Bridge using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/speed-estimation-on-bridge-using-ultralytics-yolov8.avif) |
-|                                                          Speed Estimation on Road using Ultralytics YOLOv8                                                           |                                                           Speed Estimation on Bridge using Ultralytics YOLOv8                                                            |
+| ![Speed Estimation on Road using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/speed-estimation-on-road-using-ultralytics-yolov8.avif) | ![Speed Estimation on Bridge using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/speed-estimation-on-bridge-using-ultralytics-yolov8.avif) |
+|                                                          Speed Estimation on Road using Ultralytics YOLO11                                                           |                                                           Speed Estimation on Bridge using Ultralytics YOLO11                                                            |
 
-!!! example "Speed Estimation using YOLOv8 Example"
+!!! example "Speed Estimation using YOLO11 Example"
 
     === "Speed Estimation"
 
@@ -47,7 +47,7 @@ keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         names = model.model.names
 
         cap = cv2.VideoCapture("path/to/video/file.mp4")
@@ -102,9 +102,9 @@ keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision
 
 ## FAQ
 
-### How do I estimate object speed using Ultralytics YOLOv8?
+### How do I estimate object speed using Ultralytics YOLO11?
 
-Estimating object speed with Ultralytics YOLOv8 involves combining [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking techniques. First, you need to detect objects in each frame using the YOLOv8 model. Then, track these objects across frames to calculate their movement over time. Finally, use the distance traveled by the object between frames and the frame rate to estimate its speed.
+Estimating object speed with Ultralytics YOLO11 involves combining [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking techniques. First, you need to detect objects in each frame using the YOLO11 model. Then, track these objects across frames to calculate their movement over time. Finally, use the distance traveled by the object between frames and the frame rate to estimate its speed.
 
 **Example**:
 
@@ -113,7 +113,7 @@ import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 names = model.model.names
 
 cap = cv2.VideoCapture("path/to/video/file.mp4")
@@ -142,43 +142,43 @@ cv2.destroyAllWindows()
 
 For more details, refer to our [official blog post](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects).
 
-### What are the benefits of using Ultralytics YOLOv8 for speed estimation in traffic management?
+### What are the benefits of using Ultralytics YOLO11 for speed estimation in traffic management?
 
-Using Ultralytics YOLOv8 for speed estimation offers significant advantages in traffic management:
+Using Ultralytics YOLO11 for speed estimation offers significant advantages in traffic management:
 
 - **Enhanced Safety**: Accurately estimate vehicle speeds to detect over-speeding and improve road safety.
-- **Real-Time Monitoring**: Benefit from YOLOv8's real-time object detection capability to monitor traffic flow and congestion effectively.
+- **Real-Time Monitoring**: Benefit from YOLO11's real-time object detection capability to monitor traffic flow and congestion effectively.
 - **Scalability**: Deploy the model on various hardware setups, from edge devices to servers, ensuring flexible and scalable solutions for large-scale implementations.
 
 For more applications, see [advantages of speed estimation](#advantages-of-speed-estimation).
 
-### Can YOLOv8 be integrated with other AI frameworks like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) or [PyTorch](https://www.ultralytics.com/glossary/pytorch)?
+### Can YOLO11 be integrated with other AI frameworks like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) or [PyTorch](https://www.ultralytics.com/glossary/pytorch)?
 
-Yes, YOLOv8 can be integrated with other AI frameworks like TensorFlow and PyTorch. Ultralytics provides support for exporting YOLOv8 models to various formats like ONNX, TensorRT, and CoreML, ensuring smooth interoperability with other ML frameworks.
+Yes, YOLO11 can be integrated with other AI frameworks like TensorFlow and PyTorch. Ultralytics provides support for exporting YOLO11 models to various formats like ONNX, TensorRT, and CoreML, ensuring smooth interoperability with other ML frameworks.
 
-To export a YOLOv8 model to ONNX format:
+To export a YOLO11 model to ONNX format:
 
 ```bash
-yolo export --weights yolov8n.pt --include onnx
+yolo export --weights yolo11n.pt --include onnx
 ```
 
 Learn more about exporting models in our [guide on export](../modes/export.md).
 
-### How accurate is the speed estimation using Ultralytics YOLOv8?
+### How accurate is the speed estimation using Ultralytics YOLO11?
 
-The [accuracy](https://www.ultralytics.com/glossary/accuracy) of speed estimation using Ultralytics YOLOv8 depends on several factors, including the quality of the object tracking, the resolution and frame rate of the video, and environmental variables. While the speed estimator provides reliable estimates, it may not be 100% accurate due to variances in frame processing speed and object occlusion.
+The [accuracy](https://www.ultralytics.com/glossary/accuracy) of speed estimation using Ultralytics YOLO11 depends on several factors, including the quality of the object tracking, the resolution and frame rate of the video, and environmental variables. While the speed estimator provides reliable estimates, it may not be 100% accurate due to variances in frame processing speed and object occlusion.
 
 **Note**: Always consider margin of error and validate the estimates with ground truth data when possible.
 
 For further accuracy improvement tips, check the [Arguments `SpeedEstimator` section](#arguments-speedestimator).
 
-### Why choose Ultralytics YOLOv8 over other object detection models like TensorFlow Object Detection API?
+### Why choose Ultralytics YOLO11 over other object detection models like TensorFlow Object Detection API?
 
-Ultralytics YOLOv8 offers several advantages over other object detection models, such as the TensorFlow Object Detection API:
+Ultralytics YOLO11 offers several advantages over other object detection models, such as the TensorFlow Object Detection API:
 
-- **Real-Time Performance**: YOLOv8 is optimized for real-time detection, providing high speed and accuracy.
-- **Ease of Use**: Designed with a user-friendly interface, YOLOv8 simplifies model training and deployment.
+- **Real-Time Performance**: YOLO11 is optimized for real-time detection, providing high speed and accuracy.
+- **Ease of Use**: Designed with a user-friendly interface, YOLO11 simplifies model training and deployment.
 - **Versatility**: Supports multiple tasks, including object detection, segmentation, and pose estimation.
-- **Community and Support**: YOLOv8 is backed by an active community and extensive documentation, ensuring developers have the resources they need.
+- **Community and Support**: YOLO11 is backed by an active community and extensive documentation, ensuring developers have the resources they need.
 
-For more information on the benefits of YOLOv8, explore our detailed [model page](../models/yolov8.md).
+For more information on the benefits of YOLO11, explore our detailed [model page](../models/yolov8.md).

docs/en/guides/steps-of-a-cv-project.md

@@ -166,7 +166,7 @@ Once your model has been thoroughly tested, it's time to deploy it. Deployment i
 
 - Setting Up the Environment: Configure the necessary infrastructure for your chosen deployment option, whether it's cloud-based (AWS, Google Cloud, Azure) or edge-based (local devices, IoT).
 
-- **[Exporting the Model](../modes/export.md):** Export your model to the appropriate format (e.g., ONNX, TensorRT, CoreML for YOLOv8) to ensure compatibility with your deployment platform.
+- **[Exporting the Model](../modes/export.md):** Export your model to the appropriate format (e.g., ONNX, TensorRT, CoreML for YOLO11) to ensure compatibility with your deployment platform.
 - **Deploying the Model:** Deploy the model by setting up APIs or endpoints and integrating it with your application.
 - **Ensuring Scalability**: Implement load balancers, auto-scaling groups, and monitoring tools to manage resources and handle increasing data and user requests.
 
@@ -188,12 +188,12 @@ Connecting with a community of computer vision enthusiasts can help you tackle a
 
 ### Community Resources
 
-- **GitHub Issues:** Check out the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The active community and maintainers are there to help with specific issues.
+- **GitHub Issues:** Check out the [YOLO11 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The active community and maintainers are there to help with specific issues.
 - **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to interact with other users and developers, get support, and share insights.
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Explore the [official YOLOv8 documentation](./index.md) for detailed guides with helpful tips on different computer vision tasks and projects.
+- **Ultralytics YOLO11 Documentation:** Explore the [official YOLO11 documentation](./index.md) for detailed guides with helpful tips on different computer vision tasks and projects.
 
 Using these resources will help you overcome challenges and stay updated with the latest trends and best practices in the computer vision community.
 
@@ -229,7 +229,7 @@ After splitting, apply data augmentation techniques like rotation, scaling, and
 
 ### How can I export my trained computer vision model for deployment?
 
-Exporting your model ensures compatibility with different deployment platforms. Ultralytics provides multiple formats, including ONNX, TensorRT, and CoreML. To export your YOLOv8 model, follow this guide:
+Exporting your model ensures compatibility with different deployment platforms. Ultralytics provides multiple formats, including ONNX, TensorRT, and CoreML. To export your YOLO11 model, follow this guide:
 
 - Use the `export` function with the desired format parameter.
 - Ensure the exported model fits the specifications of your deployment environment (e.g., edge devices, cloud).

docs/en/guides/streamlit-live-inference.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn how to set up a real-time object detection application using Streamlit and Ultralytics YOLOv8. Follow this step-by-step guide to implement webcam-based object detection.
-keywords: Streamlit, YOLOv8, Real-time Object Detection, Streamlit Application, YOLOv8 Streamlit Tutorial, Webcam Object Detection
+description: Learn how to set up a real-time object detection application using Streamlit and Ultralytics YOLO11. Follow this step-by-step guide to implement webcam-based object detection.
+keywords: Streamlit, YOLO11, Real-time Object Detection, Streamlit Application, YOLO11 Streamlit Tutorial, Webcam Object Detection
 ---
 
-# Live Inference with Streamlit Application using Ultralytics YOLOv8
+# Live Inference with Streamlit Application using Ultralytics YOLO11
 
 ## Introduction
 
-Streamlit makes it simple to build and deploy interactive web applications. Combining this with Ultralytics YOLOv8 allows for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and analysis directly in your browser. YOLOv8 high accuracy and speed ensure seamless performance for live video streams, making it ideal for applications in security, retail, and beyond.
+Streamlit makes it simple to build and deploy interactive web applications. Combining this with Ultralytics YOLO11 allows for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and analysis directly in your browser. YOLO11 high accuracy and speed ensure seamless performance for live video streams, making it ideal for applications in security, retail, and beyond.
 
 <p align="center">
   <br>
@@ -23,14 +23,14 @@ Streamlit makes it simple to build and deploy interactive web applications. Comb
 
 |                                                                Aquaculture                                                                 |                                                          Animals husbandry                                                           |
 | :----------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: |
-| ![Fish Detection using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/fish-detection-ultralytics-yolov8.avif) | ![Animals Detection using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/animals-detection-yolov8.avif) |
-|                                                  Fish Detection using Ultralytics YOLOv8                                                   |                                              Animals Detection using Ultralytics YOLOv8                                              |
+| ![Fish Detection using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/fish-detection-ultralytics-yolov8.avif) | ![Animals Detection using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/animals-detection-yolov8.avif) |
+|                                                  Fish Detection using Ultralytics YOLO11                                                   |                                              Animals Detection using Ultralytics YOLO11                                              |
 
 ## Advantages of Live Inference
 
-- **Seamless Real-Time Object Detection**: Streamlit combined with YOLOv8 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback.
+- **Seamless Real-Time Object Detection**: Streamlit combined with YOLO11 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback.
 - **User-Friendly Deployment**: Streamlit's interactive interface makes it easy to deploy and use the application without extensive technical knowledge. Users can start live inference with a simple click, enhancing accessibility and usability.
-- **Efficient Resource Utilization**: YOLOv8 optimized algorithm ensure high-speed processing with minimal computational resources. This efficiency allows for smooth and reliable webcam inference even on standard hardware, making advanced computer vision accessible to a wider audience.
+- **Efficient Resource Utilization**: YOLO11 optimized algorithm ensure high-speed processing with minimal computational resources. This efficiency allows for smooth and reliable webcam inference even on standard hardware, making advanced computer vision accessible to a wider audience.
 
 ## Streamlit Application Code
 
@@ -56,7 +56,7 @@ Streamlit makes it simple to build and deploy interactive web applications. Comb
         yolo streamlit-predict
         ```
 
-This will launch the Streamlit application in your default web browser. You will see the main title, subtitle, and the sidebar with configuration options. Select your desired YOLOv8 model, set the confidence and NMS thresholds, and click the "Start" button to begin the real-time object detection.
+This will launch the Streamlit application in your default web browser. You will see the main title, subtitle, and the sidebar with configuration options. Select your desired YOLO11 model, set the confidence and NMS thresholds, and click the "Start" button to begin the real-time object detection.
 
 You can optionally supply a specific model in Python:
 
@@ -75,7 +75,7 @@ You can optionally supply a specific model in Python:
 
 ## Conclusion
 
-By following this guide, you have successfully created a real-time object detection application using Streamlit and Ultralytics YOLOv8. This application allows you to experience the power of YOLOv8 in detecting objects through your webcam, with a user-friendly interface and the ability to stop the video stream at any time.
+By following this guide, you have successfully created a real-time object detection application using Streamlit and Ultralytics YOLO11. This application allows you to experience the power of YOLO11 in detecting objects through your webcam, with a user-friendly interface and the ability to stop the video stream at any time.
 
 For further enhancements, you can explore adding more features such as recording the video stream, saving the annotated frames, or integrating with other computer vision libraries.
 
@@ -90,13 +90,13 @@ Engage with the community to learn more, troubleshoot issues, and share your pro
 
 ### Official Documentation
 
-- **Ultralytics YOLOv8 Documentation:** Refer to the [official YOLOv8 documentation](https://docs.ultralytics.com/) for comprehensive guides and insights on various computer vision tasks and projects.
+- **Ultralytics YOLO11 Documentation:** Refer to the [official YOLO11 documentation](https://docs.ultralytics.com/) for comprehensive guides and insights on various computer vision tasks and projects.
 
 ## FAQ
 
-### How can I set up a real-time object detection application using Streamlit and Ultralytics YOLOv8?
+### How can I set up a real-time object detection application using Streamlit and Ultralytics YOLO11?
 
-Setting up a real-time object detection application with Streamlit and Ultralytics YOLOv8 is straightforward. First, ensure you have the Ultralytics Python package installed using:
+Setting up a real-time object detection application with Streamlit and Ultralytics YOLO11 is straightforward. First, ensure you have the Ultralytics Python package installed using:
 
 ```bash
 pip install ultralytics
@@ -124,29 +124,29 @@ Then, you can create a basic Streamlit application to run live inference:
 
 For more details on the practical setup, refer to the [Streamlit Application Code section](#streamlit-application-code) of the documentation.
 
-### What are the main advantages of using Ultralytics YOLOv8 with Streamlit for real-time object detection?
+### What are the main advantages of using Ultralytics YOLO11 with Streamlit for real-time object detection?
 
-Using Ultralytics YOLOv8 with Streamlit for real-time object detection offers several advantages:
+Using Ultralytics YOLO11 with Streamlit for real-time object detection offers several advantages:
 
 - **Seamless Real-Time Detection**: Achieve high-[accuracy](https://www.ultralytics.com/glossary/accuracy), real-time object detection directly from webcam feeds.
 - **User-Friendly Interface**: Streamlit's intuitive interface allows easy use and deployment without extensive technical knowledge.
-- **Resource Efficiency**: YOLOv8's optimized algorithms ensure high-speed processing with minimal computational resources.
+- **Resource Efficiency**: YOLO11's optimized algorithms ensure high-speed processing with minimal computational resources.
 
 Discover more about these advantages [here](#advantages-of-live-inference).
 
 ### How do I deploy a Streamlit object detection application in my web browser?
 
-After coding your Streamlit application integrating Ultralytics YOLOv8, you can deploy it by running:
+After coding your Streamlit application integrating Ultralytics YOLO11, you can deploy it by running:
 
 ```bash
 streamlit run <file-name.py>
 ```
 
-This command will launch the application in your default web browser, enabling you to select YOLOv8 models, set confidence, and NMS thresholds, and start real-time object detection with a simple click. For a detailed guide, refer to the [Streamlit Application Code](#streamlit-application-code) section.
+This command will launch the application in your default web browser, enabling you to select YOLO11 models, set confidence, and NMS thresholds, and start real-time object detection with a simple click. For a detailed guide, refer to the [Streamlit Application Code](#streamlit-application-code) section.
 
-### What are some use cases for real-time object detection using Streamlit and Ultralytics YOLOv8?
+### What are some use cases for real-time object detection using Streamlit and Ultralytics YOLO11?
 
-Real-time object detection using Streamlit and Ultralytics YOLOv8 can be applied in various sectors:
+Real-time object detection using Streamlit and Ultralytics YOLO11 can be applied in various sectors:
 
 - **Security**: Real-time monitoring for unauthorized access.
 - **Retail**: Customer counting, shelf management, and more.
@@ -154,12 +154,12 @@ Real-time object detection using Streamlit and Ultralytics YOLOv8 can be applied
 
 For more in-depth use cases and examples, explore [Ultralytics Solutions](https://docs.ultralytics.com/solutions/).
 
-### How does Ultralytics YOLOv8 compare to other object detection models like YOLOv5 and RCNNs?
+### How does Ultralytics YOLO11 compare to other object detection models like YOLOv5 and RCNNs?
 
-Ultralytics YOLOv8 provides several enhancements over prior models like YOLOv5 and RCNNs:
+Ultralytics YOLO11 provides several enhancements over prior models like YOLOv5 and RCNNs:
 
 - **Higher Speed and Accuracy**: Improved performance for real-time applications.
 - **Ease of Use**: Simplified interfaces and deployment.
 - **Resource Efficiency**: Optimized for better speed with minimal computational requirements.
 
-For a comprehensive comparison, check [Ultralytics YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8/) and related blog posts discussing model performance.
+For a comprehensive comparison, check [Ultralytics YOLO11 Documentation](https://docs.ultralytics.com/models/yolov8/) and related blog posts discussing model performance.

docs/en/guides/triton-inference-server.md

@@ -1,12 +1,12 @@
 ---
 comments: true
-description: Learn how to integrate Ultralytics YOLOv8 with NVIDIA Triton Inference Server for scalable, high-performance AI model deployment.
-keywords: Triton Inference Server, YOLOv8, Ultralytics, NVIDIA, deep learning, AI model deployment, ONNX, scalable inference
+description: Learn how to integrate Ultralytics YOLO11 with NVIDIA Triton Inference Server for scalable, high-performance AI model deployment.
+keywords: Triton Inference Server, YOLO11, Ultralytics, NVIDIA, deep learning, AI model deployment, ONNX, scalable inference
 ---
 
-# Triton Inference Server with Ultralytics YOLOv8
+# Triton Inference Server with Ultralytics YOLO11
 
-The [Triton Inference Server](https://developer.nvidia.com/triton-inference-server) (formerly known as TensorRT Inference Server) is an open-source software solution developed by NVIDIA. It provides a cloud inference solution optimized for NVIDIA GPUs. Triton simplifies the deployment of AI models at scale in production. Integrating Ultralytics YOLOv8 with Triton Inference Server allows you to deploy scalable, high-performance [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference workloads. This guide provides steps to set up and test the integration.
+The [Triton Inference Server](https://developer.nvidia.com/triton-inference-server) (formerly known as TensorRT Inference Server) is an open-source software solution developed by NVIDIA. It provides a cloud inference solution optimized for NVIDIA GPUs. Triton simplifies the deployment of AI models at scale in production. Integrating Ultralytics YOLO11 with Triton Inference Server allows you to deploy scalable, high-performance [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference workloads. This guide provides steps to set up and test the integration.
 
 <p align="center">
   <br>
@@ -38,7 +38,7 @@ Ensure you have the following prerequisites before proceeding:
     pip install tritonclient[all]
     ```
 
-## Exporting YOLOv8 to ONNX Format
+## Exporting YOLO11 to ONNX Format
 
 Before deploying the model on Triton, it must be exported to the ONNX format. ONNX (Open Neural Network Exchange) is a format that allows models to be transferred between different deep learning frameworks. Use the `export` function from the `YOLO` class:
 
@@ -46,7 +46,7 @@ Before deploying the model on Triton, it must be exported to the ONNX format. ON
 from ultralytics import YOLO
 
 # Load a model
-model = YOLO("yolov8n.pt")  # load an official model
+model = YOLO("yolo11n.pt")  # load an official model
 
 # Export the model
 onnx_file = model.export(format="onnx", dynamic=True)
@@ -141,21 +141,21 @@ subprocess.call(f"docker kill {container_id}", shell=True)
 
 ---
 
-By following the above steps, you can deploy and run Ultralytics YOLOv8 models efficiently on Triton Inference Server, providing a scalable and high-performance solution for deep learning inference tasks. If you face any issues or have further queries, refer to the [official Triton documentation](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html) or reach out to the Ultralytics community for support.
+By following the above steps, you can deploy and run Ultralytics YOLO11 models efficiently on Triton Inference Server, providing a scalable and high-performance solution for deep learning inference tasks. If you face any issues or have further queries, refer to the [official Triton documentation](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html) or reach out to the Ultralytics community for support.
 
 ## FAQ
 
-### How do I set up Ultralytics YOLOv8 with NVIDIA Triton Inference Server?
+### How do I set up Ultralytics YOLO11 with NVIDIA Triton Inference Server?
 
-Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) involves a few key steps:
+Setting up [Ultralytics YOLO11](https://docs.ultralytics.com/models/yolov8/) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) involves a few key steps:
 
-1. **Export YOLOv8 to ONNX format**:
+1. **Export YOLO11 to ONNX format**:
 
     ```python
     from ultralytics import YOLO
 
     # Load a model
-    model = YOLO("yolov8n.pt")  # load an official model
+    model = YOLO("yolo11n.pt")  # load an official model
 
     # Export the model to ONNX format
     onnx_file = model.export(format="onnx", dynamic=True)
@@ -209,21 +209,21 @@ Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) wit
         time.sleep(1)
     ```
 
-This setup can help you efficiently deploy YOLOv8 models at scale on Triton Inference Server for high-performance AI model inference.
+This setup can help you efficiently deploy YOLO11 models at scale on Triton Inference Server for high-performance AI model inference.
 
-### What benefits does using Ultralytics YOLOv8 with NVIDIA Triton Inference Server offer?
+### What benefits does using Ultralytics YOLO11 with NVIDIA Triton Inference Server offer?
 
-Integrating [Ultralytics YOLOv8](../models/yolov8.md) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) provides several advantages:
+Integrating [Ultralytics YOLO11](../models/yolov8.md) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) provides several advantages:
 
 - **Scalable AI Inference**: Triton allows serving multiple models from a single server instance, supporting dynamic model loading and unloading, making it highly scalable for diverse AI workloads.
 - **High Performance**: Optimized for NVIDIA GPUs, Triton Inference Server ensures high-speed inference operations, perfect for real-time applications such as [object detection](https://www.ultralytics.com/glossary/object-detection).
 - **Ensemble and Model Versioning**: Triton's ensemble mode enables combining multiple models to improve results, and its model versioning supports A/B testing and rolling updates.
 
-For detailed instructions on setting up and running YOLOv8 with Triton, you can refer to the [setup guide](#setting-up-triton-model-repository).
+For detailed instructions on setting up and running YOLO11 with Triton, you can refer to the [setup guide](#setting-up-triton-model-repository).
 
-### Why should I export my YOLOv8 model to ONNX format before using Triton Inference Server?
+### Why should I export my YOLO11 model to ONNX format before using Triton Inference Server?
 
-Using ONNX (Open Neural Network Exchange) format for your [Ultralytics YOLOv8](../models/yolov8.md) model before deploying it on [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) offers several key benefits:
+Using ONNX (Open Neural Network Exchange) format for your [Ultralytics YOLO11](../models/yolov8.md) model before deploying it on [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) offers several key benefits:
 
 - **Interoperability**: ONNX format supports transfer between different deep learning frameworks (such as PyTorch, TensorFlow), ensuring broader compatibility.
 - **Optimization**: Many deployment environments, including Triton, optimize for ONNX, enabling faster inference and better performance.
@@ -234,15 +234,15 @@ To export your model, use:
 ```python
 from ultralytics import YOLO
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 onnx_file = model.export(format="onnx", dynamic=True)
 ```
 
 You can follow the steps in the [exporting guide](../modes/export.md) to complete the process.
 
-### Can I run inference using the Ultralytics YOLOv8 model on Triton Inference Server?
+### Can I run inference using the Ultralytics YOLO11 model on Triton Inference Server?
 
-Yes, you can run inference using the [Ultralytics YOLOv8](../models/yolov8.md) model on [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server). Once your model is set up in the Triton Model Repository and the server is running, you can load and run inference on your model as follows:
+Yes, you can run inference using the [Ultralytics YOLO11](../models/yolov8.md) model on [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server). Once your model is set up in the Triton Model Repository and the server is running, you can load and run inference on your model as follows:
 
 ```python
 from ultralytics import YOLO
@@ -254,14 +254,14 @@ model = YOLO("http://localhost:8000/yolo", task="detect")
 results = model("path/to/image.jpg")
 ```
 
-For an in-depth guide on setting up and running Triton Server with YOLOv8, refer to the [running triton inference server](#running-triton-inference-server) section.
+For an in-depth guide on setting up and running Triton Server with YOLO11, refer to the [running triton inference server](#running-triton-inference-server) section.
 
-### How does Ultralytics YOLOv8 compare to [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and PyTorch models for deployment?
+### How does Ultralytics YOLO11 compare to [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and PyTorch models for deployment?
 
-[Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) offers several unique advantages compared to TensorFlow and PyTorch models for deployment:
+[Ultralytics YOLO11](https://docs.ultralytics.com/models/yolov8/) offers several unique advantages compared to TensorFlow and PyTorch models for deployment:
 
-- **Real-time Performance**: Optimized for real-time object detection tasks, YOLOv8 provides state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed, making it ideal for applications requiring live video analytics.
-- **Ease of Use**: YOLOv8 integrates seamlessly with Triton Inference Server and supports diverse export formats (ONNX, TensorRT, CoreML), making it flexible for various deployment scenarios.
-- **Advanced Features**: YOLOv8 includes features like dynamic model loading, model versioning, and ensemble inference, which are crucial for scalable and reliable AI deployments.
+- **Real-time Performance**: Optimized for real-time object detection tasks, YOLO11 provides state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed, making it ideal for applications requiring live video analytics.
+- **Ease of Use**: YOLO11 integrates seamlessly with Triton Inference Server and supports diverse export formats (ONNX, TensorRT, CoreML), making it flexible for various deployment scenarios.
+- **Advanced Features**: YOLO11 includes features like dynamic model loading, model versioning, and ensemble inference, which are crucial for scalable and reliable AI deployments.
 
 For more details, compare the deployment options in the [model deployment guide](../modes/export.md).

docs/en/guides/view-results-in-terminal.md

@@ -47,7 +47,7 @@ The VSCode compatible protocols for viewing images using the integrated terminal
     from ultralytics import YOLO
 
     # Load a model
-    model = YOLO("yolov8n.pt")
+    model = YOLO("yolo11n.pt")
 
     # Run inference on an image
     results = model.predict(source="ultralytics/assets/bus.jpg")
@@ -111,7 +111,7 @@ from sixel import SixelWriter
 from ultralytics import YOLO
 
 # Load a model
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 
 # Run inference on an image
 results = model.predict(source="ultralytics/assets/bus.jpg")
@@ -164,7 +164,7 @@ To view YOLO inference results in a VSCode terminal on macOS or Linux, follow th
     ```python
     from ultralytics import YOLO
 
-    model = YOLO("yolov8n.pt")
+    model = YOLO("yolo11n.pt")
     results = model.predict(source="path_to_image")
     plot = results[0].plot()
     ```

docs/en/guides/vision-eye.md

@@ -1,23 +1,23 @@
 ---
 comments: true
-description: Discover VisionEye's object mapping and tracking powered by Ultralytics YOLOv8. Simulate human eye precision, track objects, and calculate distances effortlessly.
-keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, distance calculation, computer vision, AI, machine learning, Python, tutorial
+description: Discover VisionEye's object mapping and tracking powered by Ultralytics YOLO11. Simulate human eye precision, track objects, and calculate distances effortlessly.
+keywords: VisionEye, YOLO11, Ultralytics, object mapping, object tracking, distance calculation, computer vision, AI, machine learning, Python, tutorial
 ---
 
-# VisionEye View Object Mapping using Ultralytics YOLOv8 🚀
+# VisionEye View Object Mapping using Ultralytics YOLO11 🚀
 
 ## What is VisionEye Object Mapping?
 
-[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) VisionEye offers the capability for computers to identify and pinpoint objects, simulating the observational [precision](https://www.ultralytics.com/glossary/precision) of the human eye. This functionality enables computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.
+[Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) VisionEye offers the capability for computers to identify and pinpoint objects, simulating the observational [precision](https://www.ultralytics.com/glossary/precision) of the human eye. This functionality enables computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.
 
 ## Samples
 
 |                                                                        VisionEye View                                                                        |                                                                         VisionEye View With Object Tracking                                                                         |                                                                 VisionEye View With Distance Calculation                                                                 |
 | :----------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| ![VisionEye View Object Mapping using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/visioneye-view-object-mapping-yolov8.avif) | ![VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/visioneye-object-mapping-with-tracking.avif) | ![VisionEye View with Distance Calculation using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/visioneye-distance-calculation-yolov8.avif) |
-|                                                    VisionEye View Object Mapping using Ultralytics YOLOv8                                                    |                                                     VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8                                                     |                                                    VisionEye View with Distance Calculation using Ultralytics YOLOv8                                                     |
+| ![VisionEye View Object Mapping using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/visioneye-view-object-mapping-yolov8.avif) | ![VisionEye View Object Mapping with Object Tracking using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/visioneye-object-mapping-with-tracking.avif) | ![VisionEye View with Distance Calculation using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/visioneye-distance-calculation-yolov8.avif) |
+|                                                    VisionEye View Object Mapping using Ultralytics YOLO11                                                    |                                                     VisionEye View Object Mapping with Object Tracking using Ultralytics YOLO11                                                     |                                                    VisionEye View with Distance Calculation using Ultralytics YOLO11                                                     |
 
-!!! example "VisionEye Object Mapping using YOLOv8"
+!!! example "VisionEye Object Mapping using YOLO11"
 
     === "VisionEye Object Mapping"
 
@@ -27,7 +27,7 @@ keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, dista
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         names = model.model.names
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -71,7 +71,7 @@ keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, dista
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
 
@@ -118,7 +118,7 @@ keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, dista
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator
 
-        model = YOLO("yolov8s.pt")
+        model = YOLO("yolo11n.pt")
         cap = cv2.VideoCapture("Path/to/video/file.mp4")
 
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -180,16 +180,16 @@ For any inquiries, feel free to post your questions in the [Ultralytics Issue Se
 
 ## FAQ
 
-### How do I start using VisionEye Object Mapping with Ultralytics YOLOv8?
+### How do I start using VisionEye Object Mapping with Ultralytics YOLO11?
 
-To start using VisionEye Object Mapping with Ultralytics YOLOv8, first, you'll need to install the Ultralytics YOLO package via pip. Then, you can use the sample code provided in the documentation to set up [object detection](https://www.ultralytics.com/glossary/object-detection) with VisionEye. Here's a simple example to get you started:
+To start using VisionEye Object Mapping with Ultralytics YOLO11, first, you'll need to install the Ultralytics YOLO package via pip. Then, you can use the sample code provided in the documentation to set up [object detection](https://www.ultralytics.com/glossary/object-detection) with VisionEye. Here's a simple example to get you started:
 
 ```python
 import cv2
 
 from ultralytics import YOLO
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("path/to/video/file.mp4")
 
 while True:
@@ -210,12 +210,12 @@ cap.release()
 cv2.destroyAllWindows()
 ```
 
-### What are the key features of VisionEye's object tracking capability using Ultralytics YOLOv8?
+### What are the key features of VisionEye's object tracking capability using Ultralytics YOLO11?
 
-VisionEye's object tracking with Ultralytics YOLOv8 allows users to follow the movement of objects within a video frame. Key features include:
+VisionEye's object tracking with Ultralytics YOLO11 allows users to follow the movement of objects within a video frame. Key features include:
 
 1. **Real-Time Object Tracking**: Keeps up with objects as they move.
-2. **Object Identification**: Utilizes YOLOv8's powerful detection algorithms.
+2. **Object Identification**: Utilizes YOLO11's powerful detection algorithms.
 3. **Distance Calculation**: Calculates distances between objects and specified points.
 4. **Annotation and Visualization**: Provides visual markers for tracked objects.
 
@@ -226,7 +226,7 @@ import cv2
 
 from ultralytics import YOLO
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("path/to/video/file.mp4")
 
 while True:
@@ -249,9 +249,9 @@ cv2.destroyAllWindows()
 
 For a comprehensive guide, visit the [VisionEye Object Mapping with Object Tracking](#samples).
 
-### How can I calculate distances with VisionEye's YOLOv8 model?
+### How can I calculate distances with VisionEye's YOLO11 model?
 
-Distance calculation with VisionEye and Ultralytics YOLOv8 involves determining the distance of detected objects from a specified point in the frame. It enhances spatial analysis capabilities, useful in applications such as autonomous driving and surveillance.
+Distance calculation with VisionEye and Ultralytics YOLO11 involves determining the distance of detected objects from a specified point in the frame. It enhances spatial analysis capabilities, useful in applications such as autonomous driving and surveillance.
 
 Here's a simplified example:
 
@@ -262,7 +262,7 @@ import cv2
 
 from ultralytics import YOLO
 
-model = YOLO("yolov8s.pt")
+model = YOLO("yolo11n.pt")
 cap = cv2.VideoCapture("path/to/video/file.mp4")
 center_point = (0, 480)  # Example center point
 pixel_per_meter = 10
@@ -290,19 +290,19 @@ cv2.destroyAllWindows()
 
 For detailed instructions, refer to the [VisionEye with Distance Calculation](#samples).
 
-### Why should I use Ultralytics YOLOv8 for object mapping and tracking?
+### Why should I use Ultralytics YOLO11 for object mapping and tracking?
 
-Ultralytics YOLOv8 is renowned for its speed, [accuracy](https://www.ultralytics.com/glossary/accuracy), and ease of integration, making it a top choice for object mapping and tracking. Key advantages include:
+Ultralytics YOLO11 is renowned for its speed, [accuracy](https://www.ultralytics.com/glossary/accuracy), and ease of integration, making it a top choice for object mapping and tracking. Key advantages include:
 
 1. **State-of-the-art Performance**: Delivers high accuracy in real-time object detection.
 2. **Flexibility**: Supports various tasks such as detection, tracking, and distance calculation.
 3. **Community and Support**: Extensive documentation and active GitHub community for troubleshooting and enhancements.
 4. **Ease of Use**: Intuitive API simplifies complex tasks, allowing for rapid deployment and iteration.
 
-For more information on applications and benefits, check out the [Ultralytics YOLOv8 documentation](https://docs.ultralytics.com/models/yolov8/).
+For more information on applications and benefits, check out the [Ultralytics YOLO11 documentation](https://docs.ultralytics.com/models/yolov8/).
 
 ### How can I integrate VisionEye with other [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) tools like Comet or ClearML?
 
-Ultralytics YOLOv8 can integrate seamlessly with various machine learning tools like Comet and ClearML, enhancing experiment tracking, collaboration, and reproducibility. Follow the detailed guides on [how to use YOLOv5 with Comet](https://www.ultralytics.com/blog/how-to-use-yolov5-with-comet) and [integrate YOLOv8 with ClearML](https://docs.ultralytics.com/integrations/clearml/) to get started.
+Ultralytics YOLO11 can integrate seamlessly with various machine learning tools like Comet and ClearML, enhancing experiment tracking, collaboration, and reproducibility. Follow the detailed guides on [how to use YOLOv5 with Comet](https://www.ultralytics.com/blog/how-to-use-yolov5-with-comet) and [integrate YOLO11 with ClearML](https://docs.ultralytics.com/integrations/clearml/) to get started.
 
 For further exploration and integration examples, check our [Ultralytics Integrations Guide](https://docs.ultralytics.com/integrations/).

docs/en/guides/workouts-monitoring.md

@@ -1,12 +1,12 @@
 ---
 comments: true
-description: Optimize your fitness routine with real-time workouts monitoring using Ultralytics YOLOv8. Track and improve your exercise form and performance.
-keywords: workouts monitoring, Ultralytics YOLOv8, pose estimation, fitness tracking, exercise assessment, real-time feedback, exercise form, performance metrics
+description: Optimize your fitness routine with real-time workouts monitoring using Ultralytics YOLO11. Track and improve your exercise form and performance.
+keywords: workouts monitoring, Ultralytics YOLO11, pose estimation, fitness tracking, exercise assessment, real-time feedback, exercise form, performance metrics
 ---
 
-# Workouts Monitoring using Ultralytics YOLOv8
+# Workouts Monitoring using Ultralytics YOLO11
 
-Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) enhances exercise assessment by accurately tracking key body landmarks and joints in real-time. This technology provides instant feedback on exercise form, tracks workout routines, and measures performance metrics, optimizing training sessions for users and trainers alike.
+Monitoring workouts through pose estimation with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) enhances exercise assessment by accurately tracking key body landmarks and joints in real-time. This technology provides instant feedback on exercise form, tracks workout routines, and measures performance metrics, optimizing training sessions for users and trainers alike.
 
 <p align="center">
   <br>
@@ -16,7 +16,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Workouts Monitoring using Ultralytics YOLOv8 | Pushups, Pullups, Ab Workouts
+  <strong>Watch:</strong> Workouts Monitoring using Ultralytics YOLO11 | Pushups, Pullups, Ab Workouts
 </p>
 
 ## Advantages of Workouts Monitoring?
@@ -43,7 +43,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n-pose.pt")
+        model = YOLO("yolo11n-pose.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -74,7 +74,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
 
         from ultralytics import YOLO, solutions
 
-        model = YOLO("yolov8n-pose.pt")
+        model = YOLO("yolo11n-pose.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -108,7 +108,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
 
 ### KeyPoints Map
 
-![keyPoints Order Ultralytics YOLOv8 Pose](https://github.com/ultralytics/docs/releases/download/0/keypoints-order-ultralytics-yolov8-pose.avif)
+![keyPoints Order Ultralytics YOLO11 Pose](https://github.com/ultralytics/docs/releases/download/0/keypoints-order-ultralytics-yolov8-pose.avif)
 
 ### Arguments `AIGym`
 
@@ -131,16 +131,16 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
 
 ## FAQ
 
-### How do I monitor my workouts using Ultralytics YOLOv8?
+### How do I monitor my workouts using Ultralytics YOLO11?
 
-To monitor your workouts using Ultralytics YOLOv8, you can utilize the pose estimation capabilities to track and analyze key body landmarks and joints in real-time. This allows you to receive instant feedback on your exercise form, count repetitions, and measure performance metrics. You can start by using the provided example code for pushups, pullups, or ab workouts as shown:
+To monitor your workouts using Ultralytics YOLO11, you can utilize the pose estimation capabilities to track and analyze key body landmarks and joints in real-time. This allows you to receive instant feedback on your exercise form, count repetitions, and measure performance metrics. You can start by using the provided example code for pushups, pullups, or ab workouts as shown:
 
 ```python
 import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8n-pose.pt")
+model = YOLO("yolo11n-pose.pt")
 cap = cv2.VideoCapture("path/to/video/file.mp4")
 assert cap.isOpened(), "Error reading video file"
 w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -165,9 +165,9 @@ cv2.destroyAllWindows()
 
 For further customization and settings, you can refer to the [AIGym](#arguments-aigym) section in the documentation.
 
-### What are the benefits of using Ultralytics YOLOv8 for workout monitoring?
+### What are the benefits of using Ultralytics YOLO11 for workout monitoring?
 
-Using Ultralytics YOLOv8 for workout monitoring provides several key benefits:
+Using Ultralytics YOLO11 for workout monitoring provides several key benefits:
 
 - **Optimized Performance:** By tailoring workouts based on monitoring data, you can achieve better results.
 - **Goal Achievement:** Easily track and adjust fitness goals for measurable progress.
@@ -177,13 +177,13 @@ Using Ultralytics YOLOv8 for workout monitoring provides several key benefits:
 
 You can watch a [YouTube video demonstration](https://www.youtube.com/watch?v=LGGxqLZtvuw) to see these benefits in action.
 
-### How accurate is Ultralytics YOLOv8 in detecting and tracking exercises?
+### How accurate is Ultralytics YOLO11 in detecting and tracking exercises?
 
-Ultralytics YOLOv8 is highly accurate in detecting and tracking exercises due to its state-of-the-art pose estimation capabilities. It can accurately track key body landmarks and joints, providing real-time feedback on exercise form and performance metrics. The model's pretrained weights and robust architecture ensure high [precision](https://www.ultralytics.com/glossary/precision) and reliability. For real-world examples, check out the [real-world applications](#real-world-applications) section in the documentation, which showcases pushups and pullups counting.
+Ultralytics YOLO11 is highly accurate in detecting and tracking exercises due to its state-of-the-art pose estimation capabilities. It can accurately track key body landmarks and joints, providing real-time feedback on exercise form and performance metrics. The model's pretrained weights and robust architecture ensure high [precision](https://www.ultralytics.com/glossary/precision) and reliability. For real-world examples, check out the [real-world applications](#real-world-applications) section in the documentation, which showcases pushups and pullups counting.
 
-### Can I use Ultralytics YOLOv8 for custom workout routines?
+### Can I use Ultralytics YOLO11 for custom workout routines?
 
-Yes, Ultralytics YOLOv8 can be adapted for custom workout routines. The `AIGym` class supports different pose types such as "pushup", "pullup", and "abworkout." You can specify keypoints and angles to detect specific exercises. Here is an example setup:
+Yes, Ultralytics YOLO11 can be adapted for custom workout routines. The `AIGym` class supports different pose types such as "pushup", "pullup", and "abworkout." You can specify keypoints and angles to detect specific exercises. Here is an example setup:
 
 ```python
 from ultralytics import solutions
@@ -198,7 +198,7 @@ gym_object = solutions.AIGym(
 
 For more details on setting arguments, refer to the [Arguments `AIGym`](#arguments-aigym) section. This flexibility allows you to monitor various exercises and customize routines based on your needs.
 
-### How can I save the workout monitoring output using Ultralytics YOLOv8?
+### How can I save the workout monitoring output using Ultralytics YOLO11?
 
 To save the workout monitoring output, you can modify the code to include a video writer that saves the processed frames. Here's an example:
 
@@ -207,7 +207,7 @@ import cv2
 
 from ultralytics import YOLO, solutions
 
-model = YOLO("yolov8n-pose.pt")
+model = YOLO("yolo11n-pose.pt")
 cap = cv2.VideoCapture("path/to/video/file.mp4")
 assert cap.isOpened(), "Error reading video file"
 w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -234,4 +234,4 @@ cv2.destroyAllWindows()
 video_writer.release()
 ```
 
-This setup writes the monitored video to an output file. For more details, refer to the [Workouts Monitoring with Save Output](#workouts-monitoring-using-ultralytics-yolov8) section.
+This setup writes the monitored video to an output file. For more details, refer to the [Workouts Monitoring with Save Output](#workouts-monitoring-using-ultralytics-yolo11) section.

docs/en/guides/yolo-common-issues.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Comprehensive guide to troubleshoot common YOLOv8 issues, from installation errors to model training challenges. Enhance your Ultralytics projects with our expert tips.
-keywords: YOLO, YOLOv8, troubleshooting, installation errors, model training, GPU issues, Ultralytics, AI, computer vision, deep learning, Python, CUDA, PyTorch, debugging
+description: Comprehensive guide to troubleshoot common YOLO11 issues, from installation errors to model training challenges. Enhance your Ultralytics projects with our expert tips.
+keywords: YOLO, YOLO11, troubleshooting, installation errors, model training, GPU issues, Ultralytics, AI, computer vision, deep learning, Python, CUDA, PyTorch, debugging
 ---
 
 # Troubleshooting Common YOLO Issues
@@ -12,7 +12,7 @@ keywords: YOLO, YOLOv8, troubleshooting, installation errors, model training, GP
 
 ## Introduction
 
-This guide serves as a comprehensive aid for troubleshooting common issues encountered while working with YOLOv8 on your Ultralytics projects. Navigating through these issues can be a breeze with the right guidance, ensuring your projects remain on track without unnecessary delays.
+This guide serves as a comprehensive aid for troubleshooting common issues encountered while working with YOLO11 on your Ultralytics projects. Navigating through these issues can be a breeze with the right guidance, ensuring your projects remain on track without unnecessary delays.
 
 <p align="center">
   <br>
@@ -22,7 +22,7 @@ This guide serves as a comprehensive aid for troubleshooting common issues encou
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Ultralytics YOLOv8 Common Issues | Installation Errors, Model Training Issues
+  <strong>Watch:</strong> Ultralytics YOLO11 Common Issues | Installation Errors, Model Training Issues
 </p>
 
 ## Common Issues
@@ -41,7 +41,7 @@ Installation errors can arise due to various reasons, such as incompatible versi
 
 Additionally, here are some common installation issues users have encountered, along with their respective solutions:
 
-- Import Errors or Dependency Issues - If you're getting errors during the import of YOLOv8, or you're having issues related to dependencies, consider the following troubleshooting steps:
+- Import Errors or Dependency Issues - If you're getting errors during the import of YOLO11, or you're having issues related to dependencies, consider the following troubleshooting steps:
 
     - **Fresh Installation**: Sometimes, starting with a fresh installation can resolve unexpected issues. Especially with libraries like Ultralytics, where updates might introduce changes to the file tree structure or functionalities.
 
@@ -53,7 +53,7 @@ Additionally, here are some common installation issues users have encountered, a
 
     - Remember, keeping your libraries and dependencies up-to-date is crucial for a smooth and error-free experience.
 
-- Running YOLOv8 on GPU - If you're having trouble running YOLOv8 on GPU, consider the following troubleshooting steps:
+- Running YOLO11 on GPU - If you're having trouble running YOLO11 on GPU, consider the following troubleshooting steps:
 
     - **Verify CUDA Compatibility and Installation**: Ensure your GPU is CUDA compatible and that CUDA is correctly installed. Use the `nvidia-smi` command to check the status of your NVIDIA GPU and CUDA version.
 
@@ -63,7 +63,7 @@ Additionally, here are some common installation issues users have encountered, a
 
     - **Update Your Packages**: Outdated packages might not be compatible with your GPU. Keep them updated.
 
-    - **Program Configuration**: Check if the program or code specifies GPU usage. In YOLOv8, this might be in the settings or configuration.
+    - **Program Configuration**: Check if the program or code specifies GPU usage. In YOLO11, this might be in the settings or configuration.
 
 ### Model Training Issues
 
@@ -119,7 +119,7 @@ You can access these metrics from the training logs or by using tools like Tenso
 
 **Solution**: To track and visualize training progress, you can consider using the following tools:
 
-- [TensorBoard](https://www.tensorflow.org/tensorboard): TensorBoard is a popular choice for visualizing training metrics, including loss, [accuracy](https://www.ultralytics.com/glossary/accuracy), and more. You can integrate it with your YOLOv8 training process.
+- [TensorBoard](https://www.tensorflow.org/tensorboard): TensorBoard is a popular choice for visualizing training metrics, including loss, [accuracy](https://www.ultralytics.com/glossary/accuracy), and more. You can integrate it with your YOLO11 training process.
 - [Comet](https://bit.ly/yolov8-readme-comet): Comet provides an extensive toolkit for experiment tracking and comparison. It allows you to track metrics, hyperparameters, and even model weights. Integration with YOLO models is also straightforward, providing you with a complete overview of your experiment cycle.
 - [Ultralytics HUB](https://hub.ultralytics.com/): Ultralytics HUB offers a specialized environment for tracking YOLO models, giving you a one-stop platform to manage metrics, datasets, and even collaborate with your team. Given its tailored focus on YOLO, it offers more customized tracking options.
 
@@ -177,13 +177,13 @@ Here are some things to keep in mind, if you are facing issues related to model
 
 This section will address common issues faced during model prediction.
 
-#### Getting Bounding Box Predictions With Your YOLOv8 Custom Model
+#### Getting Bounding Box Predictions With Your YOLO11 Custom Model
 
-**Issue**: When running predictions with a custom YOLOv8 model, there are challenges with the format and visualization of the bounding box coordinates.
+**Issue**: When running predictions with a custom YOLO11 model, there are challenges with the format and visualization of the bounding box coordinates.
 
 **Solution**:
 
-- Coordinate Format: YOLOv8 provides bounding box coordinates in absolute pixel values. To convert these to relative coordinates (ranging from 0 to 1), you need to divide by the image dimensions. For example, let's say your image size is 640x640. Then you would do the following:
+- Coordinate Format: YOLO11 provides bounding box coordinates in absolute pixel values. To convert these to relative coordinates (ranging from 0 to 1), you need to divide by the image dimensions. For example, let's say your image size is 640x640. Then you would do the following:
 
 ```python
 # Convert absolute coordinates to relative coordinates
@@ -195,33 +195,33 @@ y2 = y2 / 640
 
 - File Name: To obtain the file name of the image you're predicting on, access the image file path directly from the result object within your prediction loop.
 
-#### Filtering Objects in YOLOv8 Predictions
+#### Filtering Objects in YOLO11 Predictions
 
-**Issue**: Facing issues with how to filter and display only specific objects in the prediction results when running YOLOv8 using the Ultralytics library.
+**Issue**: Facing issues with how to filter and display only specific objects in the prediction results when running YOLO11 using the Ultralytics library.
 
 **Solution**: To detect specific classes use the classes argument to specify the classes you want to include in the output. For instance, to detect only cars (assuming 'cars' have class index 2):
 
 ```shell
-yolo task=detect mode=segment model=yolov8n-seg.pt source='path/to/car.mp4' show=True classes=2
+yolo task=detect mode=segment model=yolo11n-seg.pt source='path/to/car.mp4' show=True classes=2
 ```
 
-#### Understanding Precision Metrics in YOLOv8
+#### Understanding Precision Metrics in YOLO11
 
-**Issue**: Confusion regarding the difference between box precision, mask precision, and [confusion matrix](https://www.ultralytics.com/glossary/confusion-matrix) precision in YOLOv8.
+**Issue**: Confusion regarding the difference between box precision, mask precision, and [confusion matrix](https://www.ultralytics.com/glossary/confusion-matrix) precision in YOLO11.
 
 **Solution**: Box precision measures the accuracy of predicted bounding boxes compared to the actual ground truth boxes using IoU (Intersection over Union) as the metric. Mask precision assesses the agreement between predicted segmentation masks and ground truth masks in pixel-wise object classification. Confusion matrix precision, on the other hand, focuses on overall classification accuracy across all classes and does not consider the geometric accuracy of predictions. It's important to note that a [bounding box](https://www.ultralytics.com/glossary/bounding-box) can be geometrically accurate (true positive) even if the class prediction is wrong, leading to differences between box precision and confusion matrix precision. These metrics evaluate distinct aspects of a model's performance, reflecting the need for different evaluation metrics in various tasks.
 
-#### Extracting Object Dimensions in YOLOv8
+#### Extracting Object Dimensions in YOLO11
 
-**Issue**: Difficulty in retrieving the length and height of detected objects in YOLOv8, especially when multiple objects are detected in an image.
+**Issue**: Difficulty in retrieving the length and height of detected objects in YOLO11, especially when multiple objects are detected in an image.
 
-**Solution**: To retrieve the bounding box dimensions, first use the Ultralytics YOLOv8 model to predict objects in an image. Then, extract the width and height information of bounding boxes from the prediction results.
+**Solution**: To retrieve the bounding box dimensions, first use the Ultralytics YOLO11 model to predict objects in an image. Then, extract the width and height information of bounding boxes from the prediction results.
 
 ```python
 from ultralytics import YOLO
 
-# Load a pre-trained YOLOv8 model
-model = YOLO("yolov8n.pt")
+# Load a pre-trained YOLO11 model
+model = YOLO("yolo11n.pt")
 
 # Specify the source image
 source = "https://ultralytics.com/images/bus.jpg"
@@ -264,23 +264,23 @@ for box in boxes:
 
 ## Community and Support
 
-Engaging with a community of like-minded individuals can significantly enhance your experience and success in working with YOLOv8. Below are some channels and resources you may find helpful.
+Engaging with a community of like-minded individuals can significantly enhance your experience and success in working with YOLO11. Below are some channels and resources you may find helpful.
 
 ### Forums and Channels for Getting Help
 
-**GitHub Issues:** The YOLOv8 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems.
+**GitHub Issues:** The YOLO11 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems.
 
 **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and the developers.
 
 ### Official Documentation and Resources
 
-**Ultralytics YOLOv8 Docs**: The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
+**Ultralytics YOLO11 Docs**: The [official documentation](../index.md) provides a comprehensive overview of YOLO11, along with guides on installation, usage, and troubleshooting.
 
-These resources should provide a solid foundation for troubleshooting and improving your YOLOv8 projects, as well as connecting with others in the YOLOv8 community.
+These resources should provide a solid foundation for troubleshooting and improving your YOLO11 projects, as well as connecting with others in the YOLO11 community.
 
 ## Conclusion
 
-Troubleshooting is an integral part of any development process, and being equipped with the right knowledge can significantly reduce the time and effort spent in resolving issues. This guide aimed to address the most common challenges faced by users of the YOLOv8 model within the Ultralytics ecosystem. By understanding and addressing these common issues, you can ensure smoother project progress and achieve better results with your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
+Troubleshooting is an integral part of any development process, and being equipped with the right knowledge can significantly reduce the time and effort spent in resolving issues. This guide aimed to address the most common challenges faced by users of the YOLO11 model within the Ultralytics ecosystem. By understanding and addressing these common issues, you can ensure smoother project progress and achieve better results with your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
 
 Remember, the Ultralytics community is a valuable resource. Engaging with fellow developers and experts can provide additional insights and solutions that might not be covered in standard documentation. Always keep learning, experimenting, and sharing your experiences to contribute to the collective knowledge of the community.
 
@@ -288,11 +288,11 @@ Happy troubleshooting!
 
 ## FAQ
 
-### How do I resolve installation errors with YOLOv8?
+### How do I resolve installation errors with YOLO11?
 
 Installation errors can often be due to compatibility issues or missing dependencies. Ensure you use Python 3.8 or later and have PyTorch 1.8 or later installed. It's beneficial to use virtual environments to avoid conflicts. For a step-by-step installation guide, follow our [official installation guide](../quickstart.md). If you encounter import errors, try a fresh installation or update the library to the latest version.
 
-### Why is my YOLOv8 model training slow on a single GPU?
+### Why is my YOLO11 model training slow on a single GPU?
 
 Training on a single GPU might be slow due to large batch sizes or insufficient memory. To speed up training, use multiple GPUs. Ensure your system has multiple GPUs available and adjust your `.yaml` configuration file to specify the number of GPUs, e.g., `gpus: 4`. Increase the batch size accordingly to fully utilize the GPUs without exceeding memory limits. Example command:
 
@@ -300,7 +300,7 @@ Training on a single GPU might be slow due to large batch sizes or insufficient
 model.train(data="/path/to/your/data.yaml", batch=32, multi_scale=True)
 ```
 
-### How can I ensure my YOLOv8 model is training on the GPU?
+### How can I ensure my YOLO11 model is training on the GPU?
 
 If the 'device' value shows 'null' in the training logs, it generally means the training process is set to automatically use an available GPU. To explicitly assign a specific GPU, set the 'device' value in your `.yaml` configuration file. For instance:
 
@@ -310,10 +310,10 @@ device: 0
 
 This sets the training process to the first GPU. Consult the `nvidia-smi` command to confirm your CUDA setup.
 
-### How can I monitor and track my YOLOv8 model training progress?
+### How can I monitor and track my YOLO11 model training progress?
 
 Tracking and visualizing training progress can be efficiently managed through tools like [TensorBoard](https://www.tensorflow.org/tensorboard), [Comet](https://bit.ly/yolov8-readme-comet), and [Ultralytics HUB](https://hub.ultralytics.com/). These tools allow you to log and visualize metrics such as loss, [precision](https://www.ultralytics.com/glossary/precision), [recall](https://www.ultralytics.com/glossary/recall), and mAP. Implementing [early stopping](#continuous-monitoring-parameters) based on these metrics can also help achieve better training outcomes.
 
-### What should I do if YOLOv8 is not recognizing my dataset format?
+### What should I do if YOLO11 is not recognizing my dataset format?
 
 Ensure your dataset and labels conform to the expected format. Verify that annotations are accurate and of high quality. If you face any issues, refer to the [Data Collection and Annotation](https://docs.ultralytics.com/guides/data-collection-and-annotation/) guide for best practices. For more dataset-specific guidance, check the [Datasets](https://docs.ultralytics.com/datasets/) section in the documentation.

docs/en/guides/yolo-performance-metrics.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Explore essential YOLOv8 performance metrics like mAP, IoU, F1 Score, Precision, and Recall. Learn how to calculate and interpret them for model evaluation.
-keywords: YOLOv8 performance metrics, mAP, IoU, F1 Score, Precision, Recall, object detection, Ultralytics
+description: Explore essential YOLO11 performance metrics like mAP, IoU, F1 Score, Precision, and Recall. Learn how to calculate and interpret them for model evaluation.
+keywords: YOLO11 performance metrics, mAP, IoU, F1 Score, Precision, Recall, object detection, Ultralytics
 ---
 
 # Performance Metrics Deep Dive
 
 ## Introduction
 
-Performance metrics are key tools to evaluate the [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency of [object detection](https://www.ultralytics.com/glossary/object-detection) models. They shed light on how effectively a model can identify and localize objects within images. Additionally, they help in understanding the model's handling of false positives and false negatives. These insights are crucial for evaluating and enhancing the model's performance. In this guide, we will explore various performance metrics associated with YOLOv8, their significance, and how to interpret them.
+Performance metrics are key tools to evaluate the [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency of [object detection](https://www.ultralytics.com/glossary/object-detection) models. They shed light on how effectively a model can identify and localize objects within images. Additionally, they help in understanding the model's handling of false positives and false negatives. These insights are crucial for evaluating and enhancing the model's performance. In this guide, we will explore various performance metrics associated with YOLO11, their significance, and how to interpret them.
 
 <p align="center">
   <br>
@@ -18,12 +18,12 @@ Performance metrics are key tools to evaluate the [accuracy](https://www.ultraly
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong>  Ultralytics YOLOv8 Performance Metrics | MAP, F1 Score, <a href="https://www.ultralytics.com/glossary/precision">Precision</a>, IoU & Accuracy
+  <strong>Watch:</strong>  Ultralytics YOLO11 Performance Metrics | MAP, F1 Score, <a href="https://www.ultralytics.com/glossary/precision">Precision</a>, IoU & Accuracy
 </p>
 
 ## Object Detection Metrics
 
-Let's start by discussing some metrics that are not only important to YOLOv8 but are broadly applicable across different object detection models.
+Let's start by discussing some metrics that are not only important to YOLO11 but are broadly applicable across different object detection models.
 
 - **[Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU):** IoU is a measure that quantifies the overlap between a predicted [bounding box](https://www.ultralytics.com/glossary/bounding-box) and a ground truth bounding box. It plays a fundamental role in evaluating the accuracy of object localization.
 
@@ -35,9 +35,9 @@ Let's start by discussing some metrics that are not only important to YOLOv8 but
 
 - **F1 Score:** The F1 Score is the harmonic mean of precision and recall, providing a balanced assessment of a model's performance while considering both false positives and false negatives.
 
-## How to Calculate Metrics for YOLOv8 Model
+## How to Calculate Metrics for YOLO11 Model
 
-Now, we can explore [YOLOv8's Validation mode](../modes/val.md) that can be used to compute the above discussed evaluation metrics.
+Now, we can explore [YOLO11's Validation mode](../modes/val.md) that can be used to compute the above discussed evaluation metrics.
 
 Using the validation mode is simple. Once you have a trained model, you can invoke the model.val() function. This function will then process the validation dataset and return a variety of performance metrics. But what do these metrics mean? And how should you interpret them?
 
@@ -91,7 +91,7 @@ The model.val() function, apart from producing numeric metrics, also yields visu
 
 - **Validation Batch Labels (`val_batchX_labels.jpg`)**: These images depict the ground truth labels for distinct batches from the validation dataset. They provide a clear picture of what the objects are and their respective locations as per the dataset.
 
-- **Validation Batch Predictions (`val_batchX_pred.jpg`)**: Contrasting the label images, these visuals display the predictions made by the YOLOv8 model for the respective batches. By comparing these to the label images, you can easily assess how well the model detects and classifies objects visually.
+- **Validation Batch Predictions (`val_batchX_pred.jpg`)**: Contrasting the label images, these visuals display the predictions made by the YOLO11 model for the respective batches. By comparing these to the label images, you can easily assess how well the model detects and classifies objects visually.
 
 #### Results Storage
 
@@ -153,56 +153,56 @@ Real-world examples can help clarify how these metrics work in practice.
 
 ## Connect and Collaborate
 
-Tapping into a community of enthusiasts and experts can amplify your journey with YOLOv8. Here are some avenues that can facilitate learning, troubleshooting, and networking.
+Tapping into a community of enthusiasts and experts can amplify your journey with YOLO11. Here are some avenues that can facilitate learning, troubleshooting, and networking.
 
 ### Engage with the Broader Community
 
-- **GitHub Issues:** The YOLOv8 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems.
+- **GitHub Issues:** The YOLO11 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems.
 
 - **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and the developers.
 
 ### Official Documentation and Resources:
 
-- **Ultralytics YOLOv8 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
+- **Ultralytics YOLO11 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLO11, along with guides on installation, usage, and troubleshooting.
 
-Using these resources will not only guide you through any challenges but also keep you updated with the latest trends and best practices in the YOLOv8 community.
+Using these resources will not only guide you through any challenges but also keep you updated with the latest trends and best practices in the YOLO11 community.
 
 ## Conclusion
 
-In this guide, we've taken a close look at the essential performance metrics for YOLOv8. These metrics are key to understanding how well a model is performing and are vital for anyone aiming to fine-tune their models. They offer the necessary insights for improvements and to make sure the model works effectively in real-life situations.
+In this guide, we've taken a close look at the essential performance metrics for YOLO11. These metrics are key to understanding how well a model is performing and are vital for anyone aiming to fine-tune their models. They offer the necessary insights for improvements and to make sure the model works effectively in real-life situations.
 
-Remember, the YOLOv8 and Ultralytics community is an invaluable asset. Engaging with fellow developers and experts can open doors to insights and solutions not found in standard documentation. As you journey through object detection, keep the spirit of learning alive, experiment with new strategies, and share your findings. By doing so, you contribute to the community's collective wisdom and ensure its growth.
+Remember, the YOLO11 and Ultralytics community is an invaluable asset. Engaging with fellow developers and experts can open doors to insights and solutions not found in standard documentation. As you journey through object detection, keep the spirit of learning alive, experiment with new strategies, and share your findings. By doing so, you contribute to the community's collective wisdom and ensure its growth.
 
 Happy object detecting!
 
 ## FAQ
 
-### What is the significance of [Mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) in evaluating YOLOv8 model performance?
+### What is the significance of [Mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) in evaluating YOLO11 model performance?
 
-Mean Average Precision (mAP) is crucial for evaluating YOLOv8 models as it provides a single metric encapsulating precision and recall across multiple classes. mAP@0.50 measures precision at an IoU threshold of 0.50, focusing on the model's ability to detect objects correctly. mAP@0.50:0.95 averages precision across a range of IoU thresholds, offering a comprehensive assessment of detection performance. High mAP scores indicate that the model effectively balances precision and recall, essential for applications like autonomous driving and surveillance.
+Mean Average Precision (mAP) is crucial for evaluating YOLO11 models as it provides a single metric encapsulating precision and recall across multiple classes. mAP@0.50 measures precision at an IoU threshold of 0.50, focusing on the model's ability to detect objects correctly. mAP@0.50:0.95 averages precision across a range of IoU thresholds, offering a comprehensive assessment of detection performance. High mAP scores indicate that the model effectively balances precision and recall, essential for applications like autonomous driving and surveillance.
 
-### How do I interpret the Intersection over Union (IoU) value for YOLOv8 object detection?
+### How do I interpret the Intersection over Union (IoU) value for YOLO11 object detection?
 
 Intersection over Union (IoU) measures the overlap between the predicted and ground truth bounding boxes. IoU values range from 0 to 1, where higher values indicate better localization accuracy. An IoU of 1.0 means perfect alignment. Typically, an IoU threshold of 0.50 is used to define true positives in metrics like mAP. Lower IoU values suggest that the model struggles with precise object localization, which can be improved by refining bounding box regression or increasing annotation accuracy.
 
-### Why is the F1 Score important for evaluating YOLOv8 models in object detection?
+### Why is the F1 Score important for evaluating YOLO11 models in object detection?
 
-The F1 Score is important for evaluating YOLOv8 models because it provides a harmonic mean of precision and recall, balancing both false positives and false negatives. It is particularly valuable when dealing with imbalanced datasets or applications where either precision or recall alone is insufficient. A high F1 Score indicates that the model effectively detects objects while minimizing both missed detections and false alarms, making it suitable for critical applications like security systems and medical imaging.
+The F1 Score is important for evaluating YOLO11 models because it provides a harmonic mean of precision and recall, balancing both false positives and false negatives. It is particularly valuable when dealing with imbalanced datasets or applications where either precision or recall alone is insufficient. A high F1 Score indicates that the model effectively detects objects while minimizing both missed detections and false alarms, making it suitable for critical applications like security systems and medical imaging.
 
-### What are the key advantages of using Ultralytics YOLOv8 for real-time object detection?
+### What are the key advantages of using Ultralytics YOLO11 for real-time object detection?
 
-Ultralytics YOLOv8 offers multiple advantages for real-time object detection:
+Ultralytics YOLO11 offers multiple advantages for real-time object detection:
 
 - **Speed and Efficiency**: Optimized for high-speed inference, suitable for applications requiring low latency.
 - **High Accuracy**: Advanced algorithm ensures high mAP and IoU scores, balancing precision and recall.
 - **Flexibility**: Supports various tasks including object detection, segmentation, and classification.
 - **Ease of Use**: User-friendly interfaces, extensive documentation, and seamless integration with platforms like Ultralytics HUB ([HUB Quickstart](../hub/quickstart.md)).
 
-This makes YOLOv8 ideal for diverse applications from autonomous vehicles to smart city solutions.
+This makes YOLO11 ideal for diverse applications from autonomous vehicles to smart city solutions.
 
-### How can validation metrics from YOLOv8 help improve model performance?
+### How can validation metrics from YOLO11 help improve model performance?
 
-Validation metrics from YOLOv8 like precision, recall, mAP, and IoU help diagnose and improve model performance by providing insights into different aspects of detection:
+Validation metrics from YOLO11 like precision, recall, mAP, and IoU help diagnose and improve model performance by providing insights into different aspects of detection:
 
 - **Precision**: Helps identify and minimize false positives.
 - **Recall**: Ensures all relevant objects are detected.

docs/en/guides/yolo-thread-safe-inference.md

@@ -33,7 +33,7 @@ from threading import Thread
 from ultralytics import YOLO
 
 # Instantiate the model outside the thread
-shared_model = YOLO("yolov8n.pt")
+shared_model = YOLO("yolo11n.pt")
 
 
 def predict(image_path):
@@ -60,8 +60,8 @@ from threading import Thread
 from ultralytics import YOLO
 
 # Instantiate multiple models outside the thread
-shared_model_1 = YOLO("yolov8n_1.pt")
-shared_model_2 = YOLO("yolov8n_2.pt")
+shared_model_1 = YOLO("yolo11n_1.pt")
+shared_model_2 = YOLO("yolo11n_2.pt")
 
 
 def predict(model, image_path):
@@ -94,7 +94,7 @@ from ultralytics import YOLO
 
 def thread_safe_predict(image_path):
     """Predict on an image using a new YOLO model instance in a thread-safe manner; takes image path as input."""
-    local_model = YOLO("yolov8n.pt")
+    local_model = YOLO("yolo11n.pt")
     results = local_model.predict(image_path)
     # Process results
 
@@ -128,7 +128,7 @@ from ultralytics import YOLO
 
 def thread_safe_predict(image_path):
     """Predict on an image in a thread-safe manner."""
-    local_model = YOLO("yolov8n.pt")
+    local_model = YOLO("yolo11n.pt")
     results = local_model.predict(image_path)
     # Process results
 
@@ -157,7 +157,7 @@ from ultralytics import YOLO
 
 def thread_safe_predict(image_path):
     """Runs inference in a thread-safe manner with a new YOLO model instance."""
-    model = YOLO("yolov8n.pt")
+    model = YOLO("yolo11n.pt")
     results = model.predict(image_path)
     # Process results
 

docs/en/integrations/amazon-sagemaker.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn step-by-step how to deploy Ultralytics' YOLOv8 on Amazon SageMaker Endpoints, from setup to testing, for powerful real-time inference with AWS services.
-keywords: YOLOv8, Amazon SageMaker, AWS, Ultralytics, machine learning, computer vision, model deployment, AWS CloudFormation, AWS CDK, real-time inference
+description: Learn step-by-step how to deploy Ultralytics' YOLO11 on Amazon SageMaker Endpoints, from setup to testing, for powerful real-time inference with AWS services.
+keywords: YOLO11, Amazon SageMaker, AWS, Ultralytics, machine learning, computer vision, model deployment, AWS CloudFormation, AWS CDK, real-time inference
 ---
 
-# A Guide to Deploying YOLOv8 on Amazon SageMaker Endpoints
+# A Guide to Deploying YOLO11 on Amazon SageMaker Endpoints
 
-Deploying advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models like [Ultralytics' YOLOv8](https://github.com/ultralytics/ultralytics) on Amazon SageMaker Endpoints opens up a wide range of possibilities for various [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) applications. The key to effectively using these models lies in understanding their setup, configuration, and deployment processes. YOLOv8 becomes even more powerful when integrated seamlessly with Amazon SageMaker, a robust and scalable machine learning service by AWS.
+Deploying advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models like [Ultralytics' YOLO11](https://github.com/ultralytics/ultralytics) on Amazon SageMaker Endpoints opens up a wide range of possibilities for various [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) applications. The key to effectively using these models lies in understanding their setup, configuration, and deployment processes. YOLO11 becomes even more powerful when integrated seamlessly with Amazon SageMaker, a robust and scalable machine learning service by AWS.
 
-This guide will take you through the process of deploying YOLOv8 [PyTorch](https://www.ultralytics.com/glossary/pytorch) models on Amazon SageMaker Endpoints step by step. You'll learn the essentials of preparing your AWS environment, configuring the model appropriately, and using tools like AWS CloudFormation and the AWS Cloud Development Kit (CDK) for deployment.
+This guide will take you through the process of deploying YOLO11 [PyTorch](https://www.ultralytics.com/glossary/pytorch) models on Amazon SageMaker Endpoints step by step. You'll learn the essentials of preparing your AWS environment, configuring the model appropriately, and using tools like AWS CloudFormation and the AWS Cloud Development Kit (CDK) for deployment.
 
 ## Amazon SageMaker
 
@@ -18,9 +18,9 @@ This guide will take you through the process of deploying YOLOv8 [PyTorch](https
 
 [Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a machine learning service from Amazon Web Services (AWS) that simplifies the process of building, training, and deploying machine learning models. It provides a broad range of tools for handling various aspects of machine learning workflows. This includes automated features for tuning models, options for training models at scale, and straightforward methods for deploying models into production. SageMaker supports popular machine learning frameworks, offering the flexibility needed for diverse projects. Its features also cover data labeling, workflow management, and performance analysis.
 
-## Deploying YOLOv8 on Amazon SageMaker Endpoints
+## Deploying YOLO11 on Amazon SageMaker Endpoints
 
-Deploying YOLOv8 on Amazon SageMaker lets you use its managed environment for real-time inference and take advantage of features like autoscaling. Take a look at the AWS architecture below.
+Deploying YOLO11 on Amazon SageMaker lets you use its managed environment for real-time inference and take advantage of features like autoscaling. Take a look at the AWS architecture below.
 
 <p align="center">
   <img width="640" src="https://github.com/ultralytics/docs/releases/download/0/aws-architecture.avif" alt="AWS Architecture">
@@ -40,9 +40,9 @@ First, ensure you have the following prerequisites in place:
 
 - Adequate Service Quota: Confirm that you have sufficient quotas for two separate resources in Amazon SageMaker: one for `ml.m5.4xlarge` for endpoint usage and another for `ml.m5.4xlarge` for notebook instance usage. Each of these requires a minimum of one quota value. If your current quotas are below this requirement, it's important to request an increase for each. You can request a quota increase by following the detailed instructions in the [AWS Service Quotas documentation](https://docs.aws.amazon.com/servicequotas/latest/userguide/request-quota-increase.html#quota-console-increase).
 
-### Step 2: Clone the YOLOv8 SageMaker Repository
+### Step 2: Clone the YOLO11 SageMaker Repository
 
-The next step is to clone the specific AWS repository that contains the resources for deploying YOLOv8 on SageMaker. This repository, hosted on GitHub, includes the necessary CDK scripts and configuration files.
+The next step is to clone the specific AWS repository that contains the resources for deploying YOLO11 on SageMaker. This repository, hosted on GitHub, includes the necessary CDK scripts and configuration files.
 
 - Clone the GitHub Repository: Execute the following command in your terminal to clone the host-yolov8-on-sagemaker-endpoint repository:
 
@@ -104,11 +104,11 @@ cdk bootstrap
 cdk deploy
 ```
 
-### Step 5: Deploy the YOLOv8 Model
+### Step 5: Deploy the YOLO Model
 
-Before diving into the deployment instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
+Before diving into the deployment instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
 
-After creating the AWS CloudFormation Stack, the next step is to deploy YOLOv8.
+After creating the AWS CloudFormation Stack, the next step is to deploy YOLO11.
 
 - Open the Notebook Instance: Go to the AWS Console and navigate to the Amazon SageMaker service. Select "Notebook Instances" from the dashboard, then locate the notebook instance that was created by your CDK deployment script. Open the notebook instance to access the Jupyter environment.
 
@@ -136,18 +136,18 @@ def output_fn(prediction_output):
     return json.dumps(infer)
 ```
 
-- Deploy the Endpoint Using 1_DeployEndpoint.ipynb: In the Jupyter environment, open the 1_DeployEndpoint.ipynb notebook located in the sm-notebook directory. Follow the instructions in the notebook and run the cells to download the YOLOv8 model, package it with the updated inference code, and upload it to an Amazon S3 bucket. The notebook will guide you through creating and deploying a SageMaker endpoint for the YOLOv8 model.
+- Deploy the Endpoint Using 1_DeployEndpoint.ipynb: In the Jupyter environment, open the 1_DeployEndpoint.ipynb notebook located in the sm-notebook directory. Follow the instructions in the notebook and run the cells to download the YOLO11 model, package it with the updated inference code, and upload it to an Amazon S3 bucket. The notebook will guide you through creating and deploying a SageMaker endpoint for the YOLO11 model.
 
 ### Step 6: Testing Your Deployment
 
-Now that your YOLOv8 model is deployed, it's important to test its performance and functionality.
+Now that your YOLO11 model is deployed, it's important to test its performance and functionality.
 
 - Open the Test Notebook: In the same Jupyter environment, locate and open the 2_TestEndpoint.ipynb notebook, also in the sm-notebook directory.
 
 - Run the Test Notebook: Follow the instructions within the notebook to test the deployed SageMaker endpoint. This includes sending an image to the endpoint and running inferences. Then, you'll plot the output to visualize the model's performance and [accuracy](https://www.ultralytics.com/glossary/accuracy), as shown below.
 
 <p align="center">
-  <img width="640" src="https://github.com/ultralytics/docs/releases/download/0/testing-results-yolov8.avif" alt="Testing Results YOLOv8">
+  <img width="640" src="https://github.com/ultralytics/docs/releases/download/0/testing-results-yolov8.avif" alt="Testing Results YOLO11">
 </p>
 
 - Clean-Up Resources: The test notebook will also guide you through the process of cleaning up the endpoint and the hosted model. This is an important step to manage costs and resources effectively, especially if you do not plan to use the deployed model immediately.
@@ -160,24 +160,24 @@ After testing, continuous monitoring and management of your deployed model are e
 
 - Manage the Endpoint: Use the SageMaker console for ongoing management of the endpoint. This includes scaling, updating, or redeploying the model as required.
 
-By completing these steps, you will have successfully deployed and tested a YOLOv8 model on Amazon SageMaker Endpoints. This process not only equips you with practical experience in using AWS services for machine learning deployment but also lays the foundation for deploying other advanced models in the future.
+By completing these steps, you will have successfully deployed and tested a YOLO11 model on Amazon SageMaker Endpoints. This process not only equips you with practical experience in using AWS services for machine learning deployment but also lays the foundation for deploying other advanced models in the future.
 
 ## Summary
 
-This guide took you step by step through deploying YOLOv8 on Amazon SageMaker Endpoints using AWS CloudFormation and the AWS Cloud Development Kit (CDK). The process includes cloning the necessary GitHub repository, setting up the CDK environment, deploying the model using AWS services, and testing its performance on SageMaker.
+This guide took you step by step through deploying YOLO11 on Amazon SageMaker Endpoints using AWS CloudFormation and the AWS Cloud Development Kit (CDK). The process includes cloning the necessary GitHub repository, setting up the CDK environment, deploying the model using AWS services, and testing its performance on SageMaker.
 
 For more technical details, refer to [this article](https://aws.amazon.com/blogs/machine-learning/hosting-yolov8-pytorch-model-on-amazon-sagemaker-endpoints/) on the AWS Machine Learning Blog. You can also check out the official [Amazon SageMaker Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html) for more insights into various features and functionalities.
 
-Are you interested in learning more about different YOLOv8 integrations? Visit the [Ultralytics integrations guide page](../integrations/index.md) to discover additional tools and capabilities that can enhance your machine-learning projects.
+Are you interested in learning more about different YOLO11 integrations? Visit the [Ultralytics integrations guide page](../integrations/index.md) to discover additional tools and capabilities that can enhance your machine-learning projects.
 
 ## FAQ
 
-### How do I deploy the Ultralytics YOLOv8 model on Amazon SageMaker Endpoints?
+### How do I deploy the Ultralytics YOLO11 model on Amazon SageMaker Endpoints?
 
-To deploy the Ultralytics YOLOv8 model on Amazon SageMaker Endpoints, follow these steps:
+To deploy the Ultralytics YOLO11 model on Amazon SageMaker Endpoints, follow these steps:
 
 1. **Set Up Your AWS Environment**: Ensure you have an AWS Account, IAM roles with necessary permissions, and the AWS CLI configured. Install AWS CDK if not already done (refer to the [AWS CDK instructions](https://docs.aws.amazon.com/cdk/v2/guide/getting_started.html#getting_started_install)).
-2. **Clone the YOLOv8 SageMaker Repository**:
+2. **Clone the YOLO11 SageMaker Repository**:
     ```bash
     git clone https://github.com/aws-samples/host-yolov8-on-sagemaker-endpoint.git
     cd host-yolov8-on-sagemaker-endpoint/yolov8-pytorch-cdk
@@ -196,11 +196,11 @@ To deploy the Ultralytics YOLOv8 model on Amazon SageMaker Endpoints, follow the
     cdk deploy
     ```
 
-For further details, review the [documentation section](#step-5-deploy-the-yolov8-model).
+For further details, review the [documentation section](#step-5-deploy-the-yolo-model).
 
-### What are the prerequisites for deploying YOLOv8 on Amazon SageMaker?
+### What are the prerequisites for deploying YOLO11 on Amazon SageMaker?
 
-To deploy YOLOv8 on Amazon SageMaker, ensure you have the following prerequisites:
+To deploy YOLO11 on Amazon SageMaker, ensure you have the following prerequisites:
 
 1. **AWS Account**: Active AWS account ([sign up here](https://aws.amazon.com/)).
 2. **IAM Roles**: Configured IAM roles with permissions for SageMaker, CloudFormation, and Amazon S3.
@@ -210,9 +210,9 @@ To deploy YOLOv8 on Amazon SageMaker, ensure you have the following prerequisite
 
 For detailed setup, refer to [this section](#step-1-setup-your-aws-environment).
 
-### Why should I use Ultralytics YOLOv8 on Amazon SageMaker?
+### Why should I use Ultralytics YOLO11 on Amazon SageMaker?
 
-Using Ultralytics YOLOv8 on Amazon SageMaker offers several advantages:
+Using Ultralytics YOLO11 on Amazon SageMaker offers several advantages:
 
 1. **Scalability and Management**: SageMaker provides a managed environment with features like autoscaling, which helps in real-time inference needs.
 2. **Integration with AWS Services**: Seamlessly integrate with other AWS services, such as S3 for data storage, CloudFormation for infrastructure as code, and CloudWatch for monitoring.
@@ -221,9 +221,9 @@ Using Ultralytics YOLOv8 on Amazon SageMaker offers several advantages:
 
 Explore more about the advantages of using SageMaker in the [introduction section](#amazon-sagemaker).
 
-### Can I customize the inference logic for YOLOv8 on Amazon SageMaker?
+### Can I customize the inference logic for YOLO11 on Amazon SageMaker?
 
-Yes, you can customize the inference logic for YOLOv8 on Amazon SageMaker:
+Yes, you can customize the inference logic for YOLO11 on Amazon SageMaker:
 
 1. **Modify `inference.py`**: Locate and customize the `output_fn` function in the `inference.py` file to tailor output formats.
 
@@ -243,11 +243,11 @@ Yes, you can customize the inference logic for YOLOv8 on Amazon SageMaker:
 
 2. **Deploy Updated Model**: Ensure you redeploy the model using Jupyter notebooks provided (`1_DeployEndpoint.ipynb`) to include these changes.
 
-Refer to the [detailed steps](#step-5-deploy-the-yolov8-model) for deploying the modified model.
+Refer to the [detailed steps](#step-5-deploy-the-yolo-model) for deploying the modified model.
 
-### How can I test the deployed YOLOv8 model on Amazon SageMaker?
+### How can I test the deployed YOLO11 model on Amazon SageMaker?
 
-To test the deployed YOLOv8 model on Amazon SageMaker:
+To test the deployed YOLO11 model on Amazon SageMaker:
 
 1. **Open the Test Notebook**: Locate the `2_TestEndpoint.ipynb` notebook in the SageMaker Jupyter environment.
 2. **Run the Notebook**: Follow the notebook's instructions to send an image to the endpoint, perform inference, and display results.

docs/en/integrations/clearml.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Discover how to integrate YOLOv8 with ClearML to streamline your MLOps workflow, automate experiments, and enhance model management effortlessly.
-keywords: YOLOv8, ClearML, MLOps, Ultralytics, machine learning, object detection, model training, automation, experiment management
+description: Discover how to integrate YOLO11 with ClearML to streamline your MLOps workflow, automate experiments, and enhance model management effortlessly.
+keywords: YOLO11, ClearML, MLOps, Ultralytics, machine learning, object detection, model training, automation, experiment management
 ---
 
-# Training YOLOv8 with ClearML: Streamlining Your MLOps Workflow
+# Training YOLO11 with ClearML: Streamlining Your MLOps Workflow
 
 MLOps bridges the gap between creating and deploying [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models in real-world settings. It focuses on efficient deployment, scalability, and ongoing management to ensure models perform well in practical applications.
 
-[Ultralytics YOLOv8](https://www.ultralytics.com/) effortlessly integrates with ClearML, streamlining and enhancing your [object detection](https://www.ultralytics.com/glossary/object-detection) model's training and management. This guide will walk you through the integration process, detailing how to set up ClearML, manage experiments, automate model management, and collaborate effectively.
+[Ultralytics YOLO11](https://www.ultralytics.com/) effortlessly integrates with ClearML, streamlining and enhancing your [object detection](https://www.ultralytics.com/glossary/object-detection) model's training and management. This guide will walk you through the integration process, detailing how to set up ClearML, manage experiments, automate model management, and collaborate effectively.
 
 ## ClearML
 
@@ -18,9 +18,9 @@ MLOps bridges the gap between creating and deploying [machine learning](https://
 
 [ClearML](https://clear.ml/) is an innovative open-source MLOps platform that is skillfully designed to automate, monitor, and orchestrate machine learning workflows. Its key features include automated logging of all training and inference data for full experiment reproducibility, an intuitive web UI for easy [data visualization](https://www.ultralytics.com/glossary/data-visualization) and analysis, advanced hyperparameter [optimization algorithms](https://www.ultralytics.com/glossary/optimization-algorithm), and robust model management for efficient deployment across various platforms.
 
-## YOLOv8 Training with ClearML
+## YOLO11 Training with ClearML
 
-You can bring automation and efficiency to your machine learning workflow by improving your training process by integrating YOLOv8 with ClearML.
+You can bring automation and efficiency to your machine learning workflow by improving your training process by integrating YOLO11 with ClearML.
 
 ## Installation
 
@@ -31,11 +31,11 @@ To install the required packages, run:
     === "CLI"
 
         ```bash
-        # Install the required packages for YOLOv8 and ClearML
+        # Install the required packages for YOLO11 and ClearML
         pip install ultralytics clearml
         ```
 
-For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
+For detailed instructions and best practices related to the installation process, be sure to check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
 
 ## Configuring ClearML
 
@@ -56,7 +56,7 @@ After executing this command, visit the [ClearML Settings page](https://app.clea
 
 ## Usage
 
-Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
+Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
 
 !!! example "Usage"
 
@@ -70,11 +70,11 @@ Before diving into the usage instructions, be sure to check out the range of [YO
         # Step 1: Creating a ClearML Task
         task = Task.init(project_name="my_project", task_name="my_yolov8_task")
 
-        # Step 2: Selecting the YOLOv8 Model
-        model_variant = "yolov8n"
+        # Step 2: Selecting the YOLO11 Model
+        model_variant = "yolo11n"
         task.set_parameter("model_variant", model_variant)
 
-        # Step 3: Loading the YOLOv8 Model
+        # Step 3: Loading the YOLO11 Model
         model = YOLO(f"{model_variant}.pt")
 
         # Step 4: Setting Up Training Arguments
@@ -91,11 +91,11 @@ Let's understand the steps showcased in the usage code snippet above.
 
 **Step 1: Creating a ClearML Task**: A new task is initialized in ClearML, specifying your project and task names. This task will track and manage your model's training.
 
-**Step 2: Selecting the YOLOv8 Model**: The `model_variant` variable is set to 'yolov8n', one of the YOLOv8 models. This variant is then logged in ClearML for tracking.
+**Step 2: Selecting the YOLO11 Model**: The `model_variant` variable is set to 'yolo11n', one of the YOLO11 models. This variant is then logged in ClearML for tracking.
 
-**Step 3: Loading the YOLOv8 Model**: The selected YOLOv8 model is loaded using Ultralytics' YOLO class, preparing it for training.
+**Step 3: Loading the YOLO11 Model**: The selected YOLO11 model is loaded using Ultralytics' YOLO class, preparing it for training.
 
-**Step 4: Setting Up Training Arguments**: Key training arguments like the dataset (`coco8.yaml`) and the number of [epochs](https://www.ultralytics.com/glossary/epoch) (`16`) are organized in a dictionary and connected to the ClearML task. This allows for tracking and potential modification via the ClearML UI. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
+**Step 4: Setting Up Training Arguments**: Key training arguments like the dataset (`coco8.yaml`) and the number of [epochs](https://www.ultralytics.com/glossary/epoch) (`16`) are organized in a dictionary and connected to the ClearML task. This allows for tracking and potential modification via the ClearML UI. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
 
 **Step 5: Initiating Model Training**: The model training is started with the specified arguments. The results of the training process are captured in the `results` variable.
 
@@ -106,7 +106,7 @@ Upon running the usage code snippet above, you can expect the following output:
 - A confirmation message indicating the creation of a new ClearML task, along with its unique ID.
 - An informational message about the script code being stored, indicating that the code execution is being tracked by ClearML.
 - A URL link to the ClearML results page where you can monitor the training progress and view detailed logs.
-- Download progress for the YOLOv8 model and the specified dataset, followed by a summary of the model architecture and training configuration.
+- Download progress for the YOLO11 model and the specified dataset, followed by a summary of the model architecture and training configuration.
 - Initialization messages for various training components like TensorBoard, Automatic [Mixed Precision](https://www.ultralytics.com/glossary/mixed-precision) (AMP), and dataset preparation.
 - Finally, the training process starts, with progress updates as the model trains on the specified dataset. For an in-depth understanding of the performance metrics used during training, read [our guide on performance metrics](../guides/yolo-performance-metrics.md).
 
@@ -151,7 +151,7 @@ For a visual walkthrough of what the ClearML Results Page looks like, watch the
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> YOLOv8 MLOps Integration using ClearML
+  <strong>Watch:</strong> YOLO11 MLOps Integration using ClearML
 </p>
 
 ### Advanced Features in ClearML
@@ -180,7 +180,7 @@ ClearML's user-friendly interface allows easy cloning, editing, and enqueuing of
 
 ## Summary
 
-This guide has led you through the process of integrating ClearML with Ultralytics' YOLOv8. Covering everything from initial setup to advanced model management, you've discovered how to leverage ClearML for efficient training, experiment tracking, and workflow optimization in your machine learning projects.
+This guide has led you through the process of integrating ClearML with Ultralytics' YOLO11. Covering everything from initial setup to advanced model management, you've discovered how to leverage ClearML for efficient training, experiment tracking, and workflow optimization in your machine learning projects.
 
 For further details on usage, visit [ClearML's official documentation](https://clear.ml/docs/latest/docs/integrations/yolov8/).
 
@@ -188,9 +188,9 @@ Additionally, explore more integrations and capabilities of Ultralytics by visit
 
 ## FAQ
 
-### What is the process for integrating Ultralytics YOLOv8 with ClearML?
+### What is the process for integrating Ultralytics YOLO11 with ClearML?
 
-Integrating Ultralytics YOLOv8 with ClearML involves a series of steps to streamline your MLOps workflow. First, install the necessary packages:
+Integrating Ultralytics YOLO11 with ClearML involves a series of steps to streamline your MLOps workflow. First, install the necessary packages:
 
 ```bash
 pip install ultralytics clearml
@@ -202,19 +202,19 @@ Next, initialize the ClearML SDK in your environment using:
 clearml-init
 ```
 
-You then configure ClearML with your credentials from the [ClearML Settings page](https://app.clear.ml/settings/workspace-configuration). Detailed instructions on the entire setup process, including model selection and training configurations, can be found in our [YOLOv8 Model Training guide](../modes/train.md).
+You then configure ClearML with your credentials from the [ClearML Settings page](https://app.clear.ml/settings/workspace-configuration). Detailed instructions on the entire setup process, including model selection and training configurations, can be found in our [YOLO11 Model Training guide](../modes/train.md).
 
-### Why should I use ClearML with Ultralytics YOLOv8 for my machine learning projects?
+### Why should I use ClearML with Ultralytics YOLO11 for my machine learning projects?
 
-Using ClearML with Ultralytics YOLOv8 enhances your machine learning projects by automating experiment tracking, streamlining workflows, and enabling robust model management. ClearML offers real-time metrics tracking, resource utilization monitoring, and a user-friendly interface for comparing experiments. These features help optimize your model's performance and make the development process more efficient. Learn more about the benefits and procedures in our [MLOps Integration guide](../modes/train.md).
+Using ClearML with Ultralytics YOLO11 enhances your machine learning projects by automating experiment tracking, streamlining workflows, and enabling robust model management. ClearML offers real-time metrics tracking, resource utilization monitoring, and a user-friendly interface for comparing experiments. These features help optimize your model's performance and make the development process more efficient. Learn more about the benefits and procedures in our [MLOps Integration guide](../modes/train.md).
 
-### How do I troubleshoot common issues during YOLOv8 and ClearML integration?
+### How do I troubleshoot common issues during YOLO11 and ClearML integration?
 
-If you encounter issues during the integration of YOLOv8 with ClearML, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips. Typical problems might involve package installation errors, credential setup, or configuration issues. This guide provides step-by-step troubleshooting instructions to resolve these common issues efficiently.
+If you encounter issues during the integration of YOLO11 with ClearML, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips. Typical problems might involve package installation errors, credential setup, or configuration issues. This guide provides step-by-step troubleshooting instructions to resolve these common issues efficiently.
 
-### How do I set up the ClearML task for YOLOv8 model training?
+### How do I set up the ClearML task for YOLO11 model training?
 
-Setting up a ClearML task for YOLOv8 training involves initializing a task, selecting the model variant, loading the model, setting up training arguments, and finally, starting the model training. Here's a simplified example:
+Setting up a ClearML task for YOLO11 training involves initializing a task, selecting the model variant, loading the model, setting up training arguments, and finally, starting the model training. Here's a simplified example:
 
 ```python
 from clearml import Task
@@ -224,11 +224,11 @@ from ultralytics import YOLO
 # Step 1: Creating a ClearML Task
 task = Task.init(project_name="my_project", task_name="my_yolov8_task")
 
-# Step 2: Selecting the YOLOv8 Model
-model_variant = "yolov8n"
+# Step 2: Selecting the YOLO11 Model
+model_variant = "yolo11n"
 task.set_parameter("model_variant", model_variant)
 
-# Step 3: Loading the YOLOv8 Model
+# Step 3: Loading the YOLO11 Model
 model = YOLO(f"{model_variant}.pt")
 
 # Step 4: Setting Up Training Arguments
@@ -241,6 +241,6 @@ results = model.train(**args)
 
 Refer to our [Usage guide](#usage) for a detailed breakdown of these steps.
 
-### Where can I view the results of my YOLOv8 training in ClearML?
+### Where can I view the results of my YOLO11 training in ClearML?
 
-After running your YOLOv8 training script with ClearML, you can view the results on the ClearML results page. The output will include a URL link to the ClearML dashboard, where you can track metrics, compare experiments, and monitor resource usage. For more details on how to view and interpret the results, check our section on [Viewing the ClearML Results Page](#viewing-the-clearml-results-page).
+After running your YOLO11 training script with ClearML, you can view the results on the ClearML results page. The output will include a URL link to the ClearML dashboard, where you can track metrics, compare experiments, and monitor resource usage. For more details on how to view and interpret the results, check our section on [Viewing the ClearML Results Page](#viewing-the-clearml-results-page).

docs/en/integrations/comet.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn to simplify the logging of YOLOv8 training with Comet ML. This guide covers installation, setup, real-time insights, and custom logging.
-keywords: YOLOv8, Comet ML, logging, machine learning, training, model checkpoints, metrics, installation, configuration, real-time insights, custom logging
+description: Learn to simplify the logging of YOLO11 training with Comet ML. This guide covers installation, setup, real-time insights, and custom logging.
+keywords: YOLO11, Comet ML, logging, machine learning, training, model checkpoints, metrics, installation, configuration, real-time insights, custom logging
 ---
 
-# Elevating YOLOv8 Training: Simplify Your Logging Process with Comet ML
+# Elevating YOLO11 Training: Simplify Your Logging Process with Comet ML
 
 Logging key training details such as parameters, metrics, image predictions, and model checkpoints is essential in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml)—it keeps your project transparent, your progress measurable, and your results repeatable.
 
-[Ultralytics YOLOv8](https://www.ultralytics.com/) seamlessly integrates with Comet ML, efficiently capturing and optimizing every aspect of your YOLOv8 [object detection](https://www.ultralytics.com/glossary/object-detection) model's training process. In this guide, we'll cover the installation process, Comet ML setup, real-time insights, custom logging, and offline usage, ensuring that your YOLOv8 training is thoroughly documented and fine-tuned for outstanding results.
+[Ultralytics YOLO11](https://www.ultralytics.com/) seamlessly integrates with Comet ML, efficiently capturing and optimizing every aspect of your YOLO11 [object detection](https://www.ultralytics.com/glossary/object-detection) model's training process. In this guide, we'll cover the installation process, Comet ML setup, real-time insights, custom logging, and offline usage, ensuring that your YOLO11 training is thoroughly documented and fine-tuned for outstanding results.
 
 ## Comet ML
 
@@ -18,9 +18,9 @@ Logging key training details such as parameters, metrics, image predictions, and
 
 [Comet ML](https://www.comet.com/site/) is a platform for tracking, comparing, explaining, and optimizing machine learning models and experiments. It allows you to log metrics, parameters, media, and more during your model training and monitor your experiments through an aesthetically pleasing web interface. Comet ML helps data scientists iterate more rapidly, enhances transparency and reproducibility, and aids in the development of production models.
 
-## Harnessing the Power of YOLOv8 and Comet ML
+## Harnessing the Power of YOLO11 and Comet ML
 
-By combining Ultralytics YOLOv8 with Comet ML, you unlock a range of benefits. These include simplified experiment management, real-time insights for quick adjustments, flexible and tailored logging options, and the ability to log experiments offline when internet access is limited. This integration empowers you to make data-driven decisions, analyze performance metrics, and achieve exceptional results.
+By combining Ultralytics YOLO11 with Comet ML, you unlock a range of benefits. These include simplified experiment management, real-time insights for quick adjustments, flexible and tailored logging options, and the ability to log experiments offline when internet access is limited. This integration empowers you to make data-driven decisions, analyze performance metrics, and achieve exceptional results.
 
 ## Installation
 
@@ -31,7 +31,7 @@ To install the required packages, run:
     === "CLI"
 
         ```bash
-        # Install the required packages for YOLOv8 and Comet ML
+        # Install the required packages for YOLO11 and Comet ML
         pip install ultralytics comet_ml torch torchvision
         ```
 
@@ -60,7 +60,7 @@ If you are using a Google Colab notebook, the code above will prompt you to ente
 
 ## Usage
 
-Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
+Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
 
 !!! example "Usage"
 
@@ -70,7 +70,7 @@ Before diving into the usage instructions, be sure to check out the range of [YO
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Train the model
         results = model.train(
@@ -83,13 +83,13 @@ Before diving into the usage instructions, be sure to check out the range of [YO
         )
         ```
 
-After running the training code, Comet ML will create an experiment in your Comet workspace to track the run automatically. You will then be provided with a link to view the detailed logging of your [YOLOv8 model's training](../modes/train.md) process.
+After running the training code, Comet ML will create an experiment in your Comet workspace to track the run automatically. You will then be provided with a link to view the detailed logging of your [YOLO11 model's training](../modes/train.md) process.
 
 Comet automatically logs the following data with no additional configuration: metrics such as mAP and loss, hyperparameters, model checkpoints, interactive confusion matrix, and image [bounding box](https://www.ultralytics.com/glossary/bounding-box) predictions.
 
 ## Understanding Your Model's Performance with Comet ML Visualizations
 
-Let's dive into what you'll see on the Comet ML dashboard once your YOLOv8 model begins training. The dashboard is where all the action happens, presenting a range of automatically logged information through visuals and statistics. Here's a quick tour:
+Let's dive into what you'll see on the Comet ML dashboard once your YOLO11 model begins training. The dashboard is where all the action happens, presenting a range of automatically logged information through visuals and statistics. Here's a quick tour:
 
 **Experiment Panels**
 
@@ -169,19 +169,19 @@ os.environ["COMET_MODE"] = "offline"
 
 ## Summary
 
-This guide has walked you through integrating Comet ML with Ultralytics' YOLOv8. From installation to customization, you've learned to streamline experiment management, gain real-time insights, and adapt logging to your project's needs.
+This guide has walked you through integrating Comet ML with Ultralytics' YOLO11. From installation to customization, you've learned to streamline experiment management, gain real-time insights, and adapt logging to your project's needs.
 
-Explore [Comet ML's official documentation](https://www.comet.com/docs/v2/integrations/third-party-tools/yolov8/) for more insights on integrating with YOLOv8.
+Explore [Comet ML's official documentation](https://www.comet.com/docs/v2/integrations/third-party-tools/yolov8/) for more insights on integrating with YOLO11.
 
-Furthermore, if you're looking to dive deeper into the practical applications of YOLOv8, specifically for [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) tasks, this detailed guide on [fine-tuning YOLOv8 with Comet ML](https://www.comet.com/site/blog/fine-tuning-yolov8-for-image-segmentation-with-comet/) offers valuable insights and step-by-step instructions to enhance your model's performance.
+Furthermore, if you're looking to dive deeper into the practical applications of YOLO11, specifically for [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) tasks, this detailed guide on [fine-tuning YOLO11 with Comet ML](https://www.comet.com/site/blog/fine-tuning-yolov8-for-image-segmentation-with-comet/) offers valuable insights and step-by-step instructions to enhance your model's performance.
 
 Additionally, to explore other exciting integrations with Ultralytics, check out the [integration guide page](../integrations/index.md), which offers a wealth of resources and information.
 
 ## FAQ
 
-### How do I integrate Comet ML with Ultralytics YOLOv8 for training?
+### How do I integrate Comet ML with Ultralytics YOLO11 for training?
 
-To integrate Comet ML with Ultralytics YOLOv8, follow these steps:
+To integrate Comet ML with Ultralytics YOLO11, follow these steps:
 
 1. **Install the required packages**:
 
@@ -203,12 +203,12 @@ To integrate Comet ML with Ultralytics YOLOv8, follow these steps:
     comet_ml.login(project_name="comet-example-yolov8-coco128")
     ```
 
-4. **Train your YOLOv8 model and log metrics**:
+4. **Train your YOLO11 model and log metrics**:
 
     ```python
     from ultralytics import YOLO
 
-    model = YOLO("yolov8n.pt")
+    model = YOLO("yolo11n.pt")
     results = model.train(
         data="coco8.yaml",
         project="comet-example-yolov8-coco128",
@@ -221,9 +221,9 @@ To integrate Comet ML with Ultralytics YOLOv8, follow these steps:
 
 For more detailed instructions, refer to the [Comet ML configuration section](#configuring-comet-ml).
 
-### What are the benefits of using Comet ML with YOLOv8?
+### What are the benefits of using Comet ML with YOLO11?
 
-By integrating Ultralytics YOLOv8 with Comet ML, you can:
+By integrating Ultralytics YOLO11 with Comet ML, you can:
 
 - **Monitor real-time insights**: Get instant feedback on your training results, allowing for quick adjustments.
 - **Log extensive metrics**: Automatically capture essential metrics such as mAP, loss, hyperparameters, and model checkpoints.
@@ -232,7 +232,7 @@ By integrating Ultralytics YOLOv8 with Comet ML, you can:
 
 By leveraging these features, you can optimize your machine learning workflows for better performance and reproducibility. For more information, visit the [Comet ML integration guide](../integrations/index.md).
 
-### How do I customize the logging behavior of Comet ML during YOLOv8 training?
+### How do I customize the logging behavior of Comet ML during YOLO11 training?
 
 Comet ML allows for extensive customization of its logging behavior using environment variables:
 
@@ -262,9 +262,9 @@ Comet ML allows for extensive customization of its logging behavior using enviro
 
 Refer to the [Customizing Comet ML Logging](#customizing-comet-ml-logging) section for more customization options.
 
-### How do I view detailed metrics and visualizations of my YOLOv8 training on Comet ML?
+### How do I view detailed metrics and visualizations of my YOLO11 training on Comet ML?
 
-Once your YOLOv8 model starts training, you can access a wide range of metrics and visualizations on the Comet ML dashboard. Key features include:
+Once your YOLO11 model starts training, you can access a wide range of metrics and visualizations on the Comet ML dashboard. Key features include:
 
 - **Experiment Panels**: View different runs and their metrics, including segment mask loss, class loss, and mean average [precision](https://www.ultralytics.com/glossary/precision).
 - **Metrics**: Examine metrics in tabular format for detailed analysis.
@@ -273,7 +273,7 @@ Once your YOLOv8 model starts training, you can access a wide range of metrics a
 
 For a detailed overview of these features, visit the [Understanding Your Model's Performance with Comet ML Visualizations](#understanding-your-models-performance-with-comet-ml-visualizations) section.
 
-### Can I use Comet ML for offline logging when training YOLOv8 models?
+### Can I use Comet ML for offline logging when training YOLO11 models?
 
 Yes, you can enable offline logging in Comet ML by setting the `COMET_MODE` environment variable to "offline":
 

docs/en/integrations/coreml.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn how to export YOLOv8 models to CoreML for optimized, on-device machine learning on iOS and macOS. Follow step-by-step instructions.
-keywords: CoreML export, YOLOv8 models, CoreML conversion, Ultralytics, iOS object detection, macOS machine learning, AI deployment, machine learning integration
+description: Learn how to export YOLO11 models to CoreML for optimized, on-device machine learning on iOS and macOS. Follow step-by-step instructions.
+keywords: CoreML export, YOLO11 models, CoreML conversion, Ultralytics, iOS object detection, macOS machine learning, AI deployment, machine learning integration
 ---
 
-# CoreML Export for YOLOv8 Models
+# CoreML Export for YOLO11 Models
 
 Deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models on Apple devices like iPhones and Macs requires a format that ensures seamless performance.
 
-The CoreML export format allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for efficient [object detection](https://www.ultralytics.com/glossary/object-detection) in iOS and macOS applications. In this guide, we'll walk you through the steps for converting your models to the CoreML format, making it easier for your models to perform well on Apple devices.
+The CoreML export format allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for efficient [object detection](https://www.ultralytics.com/glossary/object-detection) in iOS and macOS applications. In this guide, we'll walk you through the steps for converting your models to the CoreML format, making it easier for your models to perform well on Apple devices.
 
 ## CoreML
 
@@ -40,7 +40,7 @@ Apple's CoreML framework offers robust features for on-device machine learning.
 
 ## CoreML Deployment Options
 
-Before we look at the code for exporting YOLOv8 models to the CoreML format, let's understand where CoreML models are usually used.
+Before we look at the code for exporting YOLO11 models to the CoreML format, let's understand where CoreML models are usually used.
 
 CoreML offers various deployment options for machine learning models, including:
 
@@ -52,9 +52,9 @@ CoreML offers various deployment options for machine learning models, including:
 
 - **Cloud-Based Deployment**: CoreML models are hosted on servers and accessed by the iOS app through API requests. This scalable and flexible option enables easy model updates without app revisions. It's ideal for complex models or large-scale apps requiring regular updates. However, it does require an internet connection and may pose latency and security issues.
 
-## Exporting YOLOv8 Models to CoreML
+## Exporting YOLO11 Models to CoreML
 
-Exporting YOLOv8 to CoreML enables optimized, on-device machine learning performance within Apple's ecosystem, offering benefits in terms of efficiency, security, and seamless integration with iOS, macOS, watchOS, and tvOS platforms.
+Exporting YOLO11 to CoreML enables optimized, on-device machine learning performance within Apple's ecosystem, offering benefits in terms of efficiency, security, and seamless integration with iOS, macOS, watchOS, and tvOS platforms.
 
 ### Installation
 
@@ -65,15 +65,15 @@ To install the required package, run:
     === "CLI"
 
         ```bash
-        # Install the required package for YOLOv8
+        # Install the required package for YOLO11
         pip install ultralytics
         ```
 
-For detailed instructions and best practices related to the installation process, check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
+For detailed instructions and best practices related to the installation process, check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
 
 ### Usage
 
-Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
+Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
 
 !!! example "Usage"
 
@@ -82,14 +82,14 @@ Before diving into the usage instructions, be sure to check out the range of [YO
         ```python
         from ultralytics import YOLO
 
-        # Load the YOLOv8 model
-        model = YOLO("yolov8n.pt")
+        # Load the YOLO11 model
+        model = YOLO("yolo11n.pt")
 
         # Export the model to CoreML format
-        model.export(format="coreml")  # creates 'yolov8n.mlpackage'
+        model.export(format="coreml")  # creates 'yolo11n.mlpackage'
 
         # Load the exported CoreML model
-        coreml_model = YOLO("yolov8n.mlpackage")
+        coreml_model = YOLO("yolo11n.mlpackage")
 
         # Run inference
         results = coreml_model("https://ultralytics.com/images/bus.jpg")
@@ -98,18 +98,18 @@ Before diving into the usage instructions, be sure to check out the range of [YO
     === "CLI"
 
         ```bash
-        # Export a YOLOv8n PyTorch model to CoreML format
-        yolo export model=yolov8n.pt format=coreml  # creates 'yolov8n.mlpackage''
+        # Export a YOLO11n PyTorch model to CoreML format
+        yolo export model=yolo11n.pt format=coreml  # creates 'yolo11n.mlpackage''
 
         # Run inference with the exported model
-        yolo predict model=yolov8n.mlpackage source='https://ultralytics.com/images/bus.jpg'
+        yolo predict model=yolo11n.mlpackage source='https://ultralytics.com/images/bus.jpg'
         ```
 
 For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
 
-## Deploying Exported YOLOv8 CoreML Models
+## Deploying Exported YOLO11 CoreML Models
 
-Having successfully exported your Ultralytics YOLOv8 models to CoreML, the next critical phase is deploying these models effectively. For detailed guidance on deploying CoreML models in various environments, check out these resources:
+Having successfully exported your Ultralytics YOLO11 models to CoreML, the next critical phase is deploying these models effectively. For detailed guidance on deploying CoreML models in various environments, check out these resources:
 
 - **[CoreML Tools](https://apple.github.io/coremltools/docs-guides/)**: This guide includes instructions and examples to convert models from [TensorFlow](https://www.ultralytics.com/glossary/tensorflow), PyTorch, and other libraries to Core ML.
 
@@ -119,17 +119,17 @@ Having successfully exported your Ultralytics YOLOv8 models to CoreML, the next
 
 ## Summary
 
-In this guide, we went over how to export Ultralytics YOLOv8 models to CoreML format. By following the steps outlined in this guide, you can ensure maximum compatibility and performance when exporting YOLOv8 models to CoreML.
+In this guide, we went over how to export Ultralytics YOLO11 models to CoreML format. By following the steps outlined in this guide, you can ensure maximum compatibility and performance when exporting YOLO11 models to CoreML.
 
 For further details on usage, visit the [CoreML official documentation](https://developer.apple.com/documentation/coreml).
 
-Also, if you'd like to know more about other Ultralytics YOLOv8 integrations, visit our [integration guide page](../integrations/index.md). You'll find plenty of valuable resources and insights there.
+Also, if you'd like to know more about other Ultralytics YOLO11 integrations, visit our [integration guide page](../integrations/index.md). You'll find plenty of valuable resources and insights there.
 
 ## FAQ
 
-### How do I export YOLOv8 models to CoreML format?
+### How do I export YOLO11 models to CoreML format?
 
-To export your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models to CoreML format, you'll first need to ensure you have the `ultralytics` package installed. You can install it using:
+To export your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models to CoreML format, you'll first need to ensure you have the `ultralytics` package installed. You can install it using:
 
 !!! example "Installation"
 
@@ -148,21 +148,21 @@ Next, you can export the model using the following Python or CLI commands:
         ```python
         from ultralytics import YOLO
 
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
         model.export(format="coreml")
         ```
 
     === "CLI"
 
         ```bash
-        yolo export model=yolov8n.pt format=coreml
+        yolo export model=yolo11n.pt format=coreml
         ```
 
-For further details, refer to the [Exporting YOLOv8 Models to CoreML](../modes/export.md) section of our documentation.
+For further details, refer to the [Exporting YOLO11 Models to CoreML](../modes/export.md) section of our documentation.
 
-### What are the benefits of using CoreML for deploying YOLOv8 models?
+### What are the benefits of using CoreML for deploying YOLO11 models?
 
-CoreML provides numerous advantages for deploying [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models on Apple devices:
+CoreML provides numerous advantages for deploying [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models on Apple devices:
 
 - **On-device Processing**: Enables local model inference on devices, ensuring [data privacy](https://www.ultralytics.com/glossary/data-privacy) and minimizing latency.
 - **Performance Optimization**: Leverages the full potential of the device's CPU, GPU, and Neural Engine, optimizing both speed and efficiency.
@@ -171,9 +171,9 @@ CoreML provides numerous advantages for deploying [Ultralytics YOLOv8](https://g
 
 For more details on integrating your CoreML model into an iOS app, check out the guide on [Integrating a Core ML Model into Your App](https://developer.apple.com/documentation/coreml/integrating-a-core-ml-model-into-your-app).
 
-### What are the deployment options for YOLOv8 models exported to CoreML?
+### What are the deployment options for YOLO11 models exported to CoreML?
 
-Once you export your YOLOv8 model to CoreML format, you have multiple deployment options:
+Once you export your YOLO11 model to CoreML format, you have multiple deployment options:
 
 1. **On-Device Deployment**: Directly integrate CoreML models into your app for enhanced privacy and offline functionality. This can be done as:
 
@@ -184,9 +184,9 @@ Once you export your YOLOv8 model to CoreML format, you have multiple deployment
 
 For detailed guidance on deploying CoreML models, refer to [CoreML Deployment Options](#coreml-deployment-options).
 
-### How does CoreML ensure optimized performance for YOLOv8 models?
+### How does CoreML ensure optimized performance for YOLO11 models?
 
-CoreML ensures optimized performance for [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models by utilizing various optimization techniques:
+CoreML ensures optimized performance for [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models by utilizing various optimization techniques:
 
 - **Hardware Acceleration**: Uses the device's CPU, GPU, and Neural Engine for efficient computation.
 - **Model Compression**: Provides tools for compressing models to reduce their footprint without compromising accuracy.
@@ -205,14 +205,14 @@ Yes, you can run inference directly using the exported CoreML model. Below are t
         ```python
         from ultralytics import YOLO
 
-        coreml_model = YOLO("yolov8n.mlpackage")
+        coreml_model = YOLO("yolo11n.mlpackage")
         results = coreml_model("https://ultralytics.com/images/bus.jpg")
         ```
 
     === "CLI"
 
         ```bash
-        yolo predict model=yolov8n.mlpackage source='https://ultralytics.com/images/bus.jpg'
+        yolo predict model=yolo11n.mlpackage source='https://ultralytics.com/images/bus.jpg'
         ```
 
 For additional information, refer to the [Usage section](#usage) of the CoreML export guide.

docs/en/integrations/dvc.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Unlock seamless YOLOv8 tracking with DVCLive. Discover how to log, visualize, and analyze experiments for optimized ML model performance.
-keywords: YOLOv8, DVCLive, experiment tracking, machine learning, model training, data visualization, Git integration
+description: Unlock seamless YOLO11 tracking with DVCLive. Discover how to log, visualize, and analyze experiments for optimized ML model performance.
+keywords: YOLO11, DVCLive, experiment tracking, machine learning, model training, data visualization, Git integration
 ---
 
-# Advanced YOLOv8 Experiment Tracking with DVCLive
+# Advanced YOLO11 Experiment Tracking with DVCLive
 
 Experiment tracking in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) is critical to model development and evaluation. It involves recording and analyzing various parameters, metrics, and outcomes from numerous training runs. This process is essential for understanding model performance and making data-driven decisions to refine and optimize models.
 
-Integrating DVCLive with [Ultralytics YOLOv8](https://www.ultralytics.com/) transforms the way experiments are tracked and managed. This integration offers a seamless solution for automatically logging key experiment details, comparing results across different runs, and visualizing data for in-depth analysis. In this guide, we'll understand how DVCLive can be used to streamline the process.
+Integrating DVCLive with [Ultralytics YOLO11](https://www.ultralytics.com/) transforms the way experiments are tracked and managed. This integration offers a seamless solution for automatically logging key experiment details, comparing results across different runs, and visualizing data for in-depth analysis. In this guide, we'll understand how DVCLive can be used to streamline the process.
 
 ## DVCLive
 
@@ -18,9 +18,9 @@ Integrating DVCLive with [Ultralytics YOLOv8](https://www.ultralytics.com/) tran
 
 [DVCLive](https://dvc.org/doc/dvclive), developed by DVC, is an innovative open-source tool for experiment tracking in machine learning. Integrating seamlessly with Git and DVC, it automates the logging of crucial experiment data like model parameters and training metrics. Designed for simplicity, DVCLive enables effortless comparison and analysis of multiple runs, enhancing the efficiency of machine learning projects with intuitive [data visualization](https://www.ultralytics.com/glossary/data-visualization) and analysis tools.
 
-## YOLOv8 Training with DVCLive
+## YOLO11 Training with DVCLive
 
-YOLOv8 training sessions can be effectively monitored with DVCLive. Additionally, DVC provides integral features for visualizing these experiments, including the generation of a report that enables the comparison of metric plots across all tracked experiments, offering a comprehensive view of the training process.
+YOLO11 training sessions can be effectively monitored with DVCLive. Additionally, DVC provides integral features for visualizing these experiments, including the generation of a report that enables the comparison of metric plots across all tracked experiments, offering a comprehensive view of the training process.
 
 ## Installation
 
@@ -31,11 +31,11 @@ To install the required packages, run:
     === "CLI"
 
         ```bash
-        # Install the required packages for YOLOv8 and DVCLive
+        # Install the required packages for YOLO11 and DVCLive
         pip install ultralytics dvclive
         ```
 
-For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
+For detailed instructions and best practices related to the installation process, be sure to check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
 
 ## Configuring DVCLive
 
@@ -66,27 +66,27 @@ In these commands, ensure to replace "you@example.com" with the email address as
 
 ## Usage
 
-Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
+Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
 
-### Training YOLOv8 Models with DVCLive
+### Training YOLO11 Models with DVCLive
 
-Start by running your YOLOv8 training sessions. You can use different model configurations and training parameters to suit your project needs. For instance:
+Start by running your YOLO11 training sessions. You can use different model configurations and training parameters to suit your project needs. For instance:
 
 ```bash
-# Example training commands for YOLOv8 with varying configurations
-yolo train model=yolov8n.pt data=coco8.yaml epochs=5 imgsz=512
-yolo train model=yolov8n.pt data=coco8.yaml epochs=5 imgsz=640
+# Example training commands for YOLO11 with varying configurations
+yolo train model=yolo11n.pt data=coco8.yaml epochs=5 imgsz=512
+yolo train model=yolo11n.pt data=coco8.yaml epochs=5 imgsz=640
 ```
 
-Adjust the model, data, [epochs](https://www.ultralytics.com/glossary/epoch), and imgsz parameters according to your specific requirements. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
+Adjust the model, data, [epochs](https://www.ultralytics.com/glossary/epoch), and imgsz parameters according to your specific requirements. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
 
 ### Monitoring Experiments with DVCLive
 
-DVCLive enhances the training process by enabling the tracking and visualization of key metrics. When installed, Ultralytics YOLOv8 automatically integrates with DVCLive for experiment tracking, which you can later analyze for performance insights. For a comprehensive understanding of the specific performance metrics used during training, be sure to explore [our detailed guide on performance metrics](../guides/yolo-performance-metrics.md).
+DVCLive enhances the training process by enabling the tracking and visualization of key metrics. When installed, Ultralytics YOLO11 automatically integrates with DVCLive for experiment tracking, which you can later analyze for performance insights. For a comprehensive understanding of the specific performance metrics used during training, be sure to explore [our detailed guide on performance metrics](../guides/yolo-performance-metrics.md).
 
 ### Analyzing Results
 
-After your YOLOv8 training sessions are complete, you can leverage DVCLive's powerful visualization tools for in-depth analysis of the results. DVCLive's integration ensures that all training metrics are systematically logged, facilitating a comprehensive evaluation of your model's performance.
+After your YOLO11 training sessions are complete, you can leverage DVCLive's powerful visualization tools for in-depth analysis of the results. DVCLive's integration ensures that all training metrics are systematically logged, facilitating a comprehensive evaluation of your model's performance.
 
 To start the analysis, you can extract the experiment data using DVC's API and process it with Pandas for easier handling and visualization:
 
@@ -108,7 +108,7 @@ df.reset_index(drop=True, inplace=True)
 print(df)
 ```
 
-The output of the code snippet above provides a clear tabular view of the different experiments conducted with YOLOv8 models. Each row represents a different training run, detailing the experiment's name, the number of epochs, image size (imgsz), the specific model used, and the mAP50-95(B) metric. This metric is crucial for evaluating the model's [accuracy](https://www.ultralytics.com/glossary/accuracy), with higher values indicating better performance.
+The output of the code snippet above provides a clear tabular view of the different experiments conducted with YOLO11 models. Each row represents a different training run, detailing the experiment's name, the number of epochs, image size (imgsz), the specific model used, and the mAP50-95(B) metric. This metric is crucial for evaluating the model's [accuracy](https://www.ultralytics.com/glossary/accuracy), with higher values indicating better performance.
 
 #### Visualizing Results with Plotly
 
@@ -164,7 +164,7 @@ Based on your analysis, iterate on your experiments. Adjust model configurations
 
 ## Summary
 
-This guide has led you through the process of integrating DVCLive with Ultralytics' YOLOv8. You have learned how to harness the power of DVCLive for detailed experiment monitoring, effective visualization, and insightful analysis in your machine learning endeavors.
+This guide has led you through the process of integrating DVCLive with Ultralytics' YOLO11. You have learned how to harness the power of DVCLive for detailed experiment monitoring, effective visualization, and insightful analysis in your machine learning endeavors.
 
 For further details on usage, visit [DVCLive's official documentation](https://dvc.org/doc/dvclive/ml-frameworks/yolo).
 
@@ -172,9 +172,9 @@ Additionally, explore more integrations and capabilities of Ultralytics by visit
 
 ## FAQ
 
-### How do I integrate DVCLive with Ultralytics YOLOv8 for experiment tracking?
+### How do I integrate DVCLive with Ultralytics YOLO11 for experiment tracking?
 
-Integrating DVCLive with Ultralytics YOLOv8 is straightforward. Start by installing the necessary packages:
+Integrating DVCLive with Ultralytics YOLO11 is straightforward. Start by installing the necessary packages:
 
 !!! example "Installation"
 
@@ -198,21 +198,21 @@ Next, initialize a Git repository and configure DVCLive in your project:
         git commit -m "DVC init"
         ```
 
-Follow our [YOLOv8 Installation guide](../quickstart.md) for detailed setup instructions.
+Follow our [YOLO11 Installation guide](../quickstart.md) for detailed setup instructions.
 
-### Why should I use DVCLive for tracking YOLOv8 experiments?
+### Why should I use DVCLive for tracking YOLO11 experiments?
 
-Using DVCLive with YOLOv8 provides several advantages, such as:
+Using DVCLive with YOLO11 provides several advantages, such as:
 
 - **Automated Logging**: DVCLive automatically records key experiment details like model parameters and metrics.
 - **Easy Comparison**: Facilitates comparison of results across different runs.
 - **Visualization Tools**: Leverages DVCLive's robust data visualization capabilities for in-depth analysis.
 
-For further details, refer to our guide on [YOLOv8 Model Training](../modes/train.md) and [YOLO Performance Metrics](../guides/yolo-performance-metrics.md) to maximize your experiment tracking efficiency.
+For further details, refer to our guide on [YOLO11 Model Training](../modes/train.md) and [YOLO Performance Metrics](../guides/yolo-performance-metrics.md) to maximize your experiment tracking efficiency.
 
-### How can DVCLive improve my results analysis for YOLOv8 training sessions?
+### How can DVCLive improve my results analysis for YOLO11 training sessions?
 
-After completing your YOLOv8 training sessions, DVCLive helps in visualizing and analyzing the results effectively. Example code for loading and displaying experiment data:
+After completing your YOLO11 training sessions, DVCLive helps in visualizing and analyzing the results effectively. Example code for loading and displaying experiment data:
 
 ```python
 import dvc.api
@@ -241,11 +241,11 @@ fig = parallel_coordinates(df, columns, color="metrics.mAP50-95(B)")
 fig.show()
 ```
 
-Refer to our guide on [YOLOv8 Training with DVCLive](#yolov8-training-with-dvclive) for more examples and best practices.
+Refer to our guide on [YOLO11 Training with DVCLive](#yolo11-training-with-dvclive) for more examples and best practices.
 
-### What are the steps to configure my environment for DVCLive and YOLOv8 integration?
+### What are the steps to configure my environment for DVCLive and YOLO11 integration?
 
-To configure your environment for a smooth integration of DVCLive and YOLOv8, follow these steps:
+To configure your environment for a smooth integration of DVCLive and YOLO11, follow these steps:
 
 1. **Install Required Packages**: Use `pip install ultralytics dvclive`.
 2. **Initialize Git Repository**: Run `git init -q`.
@@ -254,9 +254,9 @@ To configure your environment for a smooth integration of DVCLive and YOLOv8, fo
 
 These steps ensure proper version control and setup for experiment tracking. For in-depth configuration details, visit our [Configuration guide](../quickstart.md).
 
-### How do I visualize YOLOv8 experiment results using DVCLive?
+### How do I visualize YOLO11 experiment results using DVCLive?
 
-DVCLive offers powerful tools to visualize the results of YOLOv8 experiments. Here's how you can generate comparative plots:
+DVCLive offers powerful tools to visualize the results of YOLO11 experiments. Here's how you can generate comparative plots:
 
 !!! example "Generate Comparative Plots"
 
@@ -275,4 +275,4 @@ from IPython.display import HTML
 HTML(filename="./dvc_plots/index.html")
 ```
 
-These visualizations help identify trends and optimize model performance. Check our detailed guides on [YOLOv8 Experiment Analysis](#analyzing-results) for comprehensive steps and examples.
+These visualizations help identify trends and optimize model performance. Check our detailed guides on [YOLO11 Experiment Analysis](#analyzing-results) for comprehensive steps and examples.

docs/en/integrations/edge-tpu.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn how to export YOLOv8 models to TFLite Edge TPU format for high-speed, low-power inferencing on mobile and embedded devices.
-keywords: YOLOv8, TFLite Edge TPU, TensorFlow Lite, model export, machine learning, edge computing, neural networks, Ultralytics
+description: Learn how to export YOLO11 models to TFLite Edge TPU format for high-speed, low-power inferencing on mobile and embedded devices.
+keywords: YOLO11, TFLite Edge TPU, TensorFlow Lite, model export, machine learning, edge computing, neural networks, Ultralytics
 ---
 
-# Learn to Export to TFLite Edge TPU Format From YOLOv8 Model
+# Learn to Export to TFLite Edge TPU Format From YOLO11 Model
 
 Deploying computer vision models on devices with limited computational power, such as mobile or embedded systems, can be tricky. Using a model format that is optimized for faster performance simplifies the process. The [TensorFlow Lite](https://ai.google.dev/edge/litert) [Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) or TFLite Edge TPU model format is designed to use minimal power while delivering fast performance for neural networks.
 
-The export to TFLite Edge TPU format feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for high-speed and low-power inferencing. In this guide, we'll walk you through converting your models to the TFLite Edge TPU format, making it easier for your models to perform well on various mobile and embedded devices.
+The export to TFLite Edge TPU format feature allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for high-speed and low-power inferencing. In this guide, we'll walk you through converting your models to the TFLite Edge TPU format, making it easier for your models to perform well on various mobile and embedded devices.
 
 ## Why Should You Export to TFLite Edge TPU?
 
@@ -32,7 +32,7 @@ Here are the key features that make TFLite Edge TPU a great model format choice
 
 ## Deployment Options with TFLite Edge TPU
 
-Before we jump into how to export YOLOv8 models to the TFLite Edge TPU format, let's understand where TFLite Edge TPU models are usually used.
+Before we jump into how to export YOLO11 models to the TFLite Edge TPU format, let's understand where TFLite Edge TPU models are usually used.
 
 TFLite Edge TPU offers various deployment options for machine learning models, including:
 
@@ -42,9 +42,9 @@ TFLite Edge TPU offers various deployment options for machine learning models, i
 
 - **Hybrid Deployment**: A hybrid approach combines on-device and cloud deployment and offers a versatile and scalable solution for deploying machine learning models. Advantages include on-device processing for quick responses and [cloud computing](https://www.ultralytics.com/glossary/cloud-computing) for more complex computations.
 
-## Exporting YOLOv8 Models to TFLite Edge TPU
+## Exporting YOLO11 Models to TFLite Edge TPU
 
-You can expand model compatibility and deployment flexibility by converting YOLOv8 models to TensorFlow Edge TPU.
+You can expand model compatibility and deployment flexibility by converting YOLO11 models to TensorFlow Edge TPU.
 
 ### Installation
 
@@ -55,15 +55,15 @@ To install the required package, run:
     === "CLI"
 
         ```bash
-        # Install the required package for YOLOv8
+        # Install the required package for YOLO11
         pip install ultralytics
         ```
 
-For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
+For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
 
 ### Usage
 
-Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
+Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
 
 !!! example "Usage"
 
@@ -72,14 +72,14 @@ Before diving into the usage instructions, it's important to note that while all
         ```python
         from ultralytics import YOLO
 
-        # Load the YOLOv8 model
-        model = YOLO("yolov8n.pt")
+        # Load the YOLO11 model
+        model = YOLO("yolo11n.pt")
 
         # Export the model to TFLite Edge TPU format
-        model.export(format="edgetpu")  # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
+        model.export(format="edgetpu")  # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
 
         # Load the exported TFLite Edge TPU model
-        edgetpu_model = YOLO("yolov8n_full_integer_quant_edgetpu.tflite")
+        edgetpu_model = YOLO("yolo11n_full_integer_quant_edgetpu.tflite")
 
         # Run inference
         results = edgetpu_model("https://ultralytics.com/images/bus.jpg")
@@ -88,22 +88,22 @@ Before diving into the usage instructions, it's important to note that while all
     === "CLI"
 
         ```bash
-        # Export a YOLOv8n PyTorch model to TFLite Edge TPU format
-        yolo export model=yolov8n.pt format=edgetpu  # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
+        # Export a YOLO11n PyTorch model to TFLite Edge TPU format
+        yolo export model=yolo11n.pt format=edgetpu  # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
 
         # Run inference with the exported model
-        yolo predict model=yolov8n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
+        yolo predict model=yolo11n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
         ```
 
 For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
 
-## Deploying Exported YOLOv8 TFLite Edge TPU Models
+## Deploying Exported YOLO11 TFLite Edge TPU Models
 
-After successfully exporting your Ultralytics YOLOv8 models to TFLite Edge TPU format, you can now deploy them. The primary and recommended first step for running a TFLite Edge TPU model is to use the YOLO("model_edgetpu.tflite") method, as outlined in the previous usage code snippet.
+After successfully exporting your Ultralytics YOLO11 models to TFLite Edge TPU format, you can now deploy them. The primary and recommended first step for running a TFLite Edge TPU model is to use the YOLO("model_edgetpu.tflite") method, as outlined in the previous usage code snippet.
 
 However, for in-depth instructions on deploying your TFLite Edge TPU models, take a look at the following resources:
 
-- **[Coral Edge TPU on a Raspberry Pi with Ultralytics YOLOv8](../guides/coral-edge-tpu-on-raspberry-pi.md)**: Discover how to integrate Coral Edge TPUs with Raspberry Pi for enhanced machine learning capabilities.
+- **[Coral Edge TPU on a Raspberry Pi with Ultralytics YOLO11](../guides/coral-edge-tpu-on-raspberry-pi.md)**: Discover how to integrate Coral Edge TPUs with Raspberry Pi for enhanced machine learning capabilities.
 
 - **[Code Examples](https://coral.ai/docs/edgetpu/compiler/)**: Access practical TensorFlow Edge TPU deployment examples to kickstart your projects.
 
@@ -111,17 +111,17 @@ However, for in-depth instructions on deploying your TFLite Edge TPU models, tak
 
 ## Summary
 
-In this guide, we've learned how to export Ultralytics YOLOv8 models to TFLite Edge TPU format. By following the steps mentioned above, you can increase the speed and power of your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications.
+In this guide, we've learned how to export Ultralytics YOLO11 models to TFLite Edge TPU format. By following the steps mentioned above, you can increase the speed and power of your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications.
 
 For further details on usage, visit the [Edge TPU official website](https://cloud.google.com/tpu).
 
-Also, for more information on other Ultralytics YOLOv8 integrations, please visit our [integration guide page](index.md). There, you'll discover valuable resources and insights.
+Also, for more information on other Ultralytics YOLO11 integrations, please visit our [integration guide page](index.md). There, you'll discover valuable resources and insights.
 
 ## FAQ
 
-### How do I export a YOLOv8 model to TFLite Edge TPU format?
+### How do I export a YOLO11 model to TFLite Edge TPU format?
 
-To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
+To export a YOLO11 model to TFLite Edge TPU format, you can follow these steps:
 
 !!! example "Usage"
 
@@ -130,14 +130,14 @@ To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
         ```python
         from ultralytics import YOLO
 
-        # Load the YOLOv8 model
-        model = YOLO("yolov8n.pt")
+        # Load the YOLO11 model
+        model = YOLO("yolo11n.pt")
 
         # Export the model to TFLite Edge TPU format
-        model.export(format="edgetpu")  # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
+        model.export(format="edgetpu")  # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
 
         # Load the exported TFLite Edge TPU model
-        edgetpu_model = YOLO("yolov8n_full_integer_quant_edgetpu.tflite")
+        edgetpu_model = YOLO("yolo11n_full_integer_quant_edgetpu.tflite")
 
         # Run inference
         results = edgetpu_model("https://ultralytics.com/images/bus.jpg")
@@ -146,18 +146,18 @@ To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
     === "CLI"
 
         ```bash
-        # Export a YOLOv8n PyTorch model to TFLite Edge TPU format
-        yolo export model=yolov8n.pt format=edgetpu  # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
+        # Export a YOLO11n PyTorch model to TFLite Edge TPU format
+        yolo export model=yolo11n.pt format=edgetpu  # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
 
         # Run inference with the exported model
-        yolo predict model=yolov8n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
+        yolo predict model=yolo11n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
         ```
 
 For complete details on exporting models to other formats, refer to our [export guide](../modes/export.md).
 
-### What are the benefits of exporting YOLOv8 models to TFLite Edge TPU?
+### What are the benefits of exporting YOLO11 models to TFLite Edge TPU?
 
-Exporting YOLOv8 models to TFLite Edge TPU offers several benefits:
+Exporting YOLO11 models to TFLite Edge TPU offers several benefits:
 
 - **Optimized Performance**: Achieve high-speed neural network performance with minimal power consumption.
 - **Reduced Latency**: Quick local data processing without the need for cloud dependency.

docs/en/integrations/google-colab.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Learn how to efficiently train Ultralytics YOLOv8 models using Google Colab's powerful cloud-based environment. Start your project with ease.
-keywords: YOLOv8, Google Colab, machine learning, deep learning, model training, GPU, TPU, cloud computing, Jupyter Notebook, Ultralytics
+description: Learn how to efficiently train Ultralytics YOLO11 models using Google Colab's powerful cloud-based environment. Start your project with ease.
+keywords: YOLO11, Google Colab, machine learning, deep learning, model training, GPU, TPU, cloud computing, Jupyter Notebook, Ultralytics
 ---
 
-# Accelerating YOLOv8 Projects with Google Colab
+# Accelerating YOLO11 Projects with Google Colab
 
 Many developers lack the powerful computing resources needed to build [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models. Acquiring high-end hardware or renting a decent GPU can be expensive. Google Colab is a great solution to this. It's a browser-based platform that allows you to work with large datasets, develop complex models, and share your work with others without a huge cost.
 
-You can use Google Colab to work on projects related to [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models. Google Colab's user-friendly environment is well suited for efficient model development and experimentation. Let's learn more about Google Colab, its key features, and how you can use it to train YOLOv8 models.
+You can use Google Colab to work on projects related to [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models. Google Colab's user-friendly environment is well suited for efficient model development and experimentation. Let's learn more about Google Colab, its key features, and how you can use it to train YOLO11 models.
 
 ## Google Colaboratory
 
@@ -16,15 +16,15 @@ Google Colaboratory, commonly known as Google Colab, was developed by Google Res
 
 You can use Google Colab regardless of the specifications and configurations of your local computer. All you need is a Google account and a web browser, and you're good to go.
 
-## Training YOLOv8 Using Google Colaboratory
+## Training YOLO11 Using Google Colaboratory
 
-Training YOLOv8 models on Google Colab is pretty straightforward. Thanks to the integration, you can access the [Google Colab YOLOv8 Notebook](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb) and start training your model immediately. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
+Training YOLO11 models on Google Colab is pretty straightforward. Thanks to the integration, you can access the [Google Colab YOLO11 Notebook](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb) and start training your model immediately. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
 
 Sign in to your Google account and run the notebook's cells to train your model.
 
-![Training YOLOv8 Using Google Colab](https://github.com/ultralytics/docs/releases/download/0/training-yolov8-using-google-colab.avif)
+![Training YOLO11 Using Google Colab](https://github.com/ultralytics/docs/releases/download/0/training-yolov8-using-google-colab.avif)
 
-Learn how to train a YOLOv8 model with custom data on YouTube with Nicolai. Check out the guide below.
+Learn how to train a YOLO11 model with custom data on YouTube with Nicolai. Check out the guide below.
 
 <p align="center">
   <br>
@@ -34,7 +34,7 @@ Learn how to train a YOLOv8 model with custom data on YouTube with Nicolai. Chec
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> How to Train Ultralytics YOLOv8 models on Your Custom Dataset in Google Colab | Episode 3
+  <strong>Watch:</strong> How to Train Ultralytics YOLO11 models on Your Custom Dataset in Google Colab | Episode 3
 </p>
 
 ### Common Questions While Working with Google Colab
@@ -75,9 +75,9 @@ Now, let's look at some of the standout features that make Google Colab a go-to
 
 - **Educational Resources:** Google Colab offers a range of tutorials and example notebooks to help users learn and explore various functionalities.
 
-## Why Should You Use Google Colab for Your YOLOv8 Projects?
+## Why Should You Use Google Colab for Your YOLO11 Projects?
 
-There are many options for training and evaluating YOLOv8 models, so what makes the integration with Google Colab unique? Let's explore the advantages of this integration:
+There are many options for training and evaluating YOLO11 models, so what makes the integration with Google Colab unique? Let's explore the advantages of this integration:
 
 - **Zero Setup:** Since Colab runs in the cloud, users can start training models immediately without the need for complex environment setups. Just create an account and start coding.
 
@@ -95,7 +95,7 @@ There are many options for training and evaluating YOLOv8 models, so what makes
 
 If you'd like to dive deeper into Google Colab, here are a few resources to guide you.
 
-- **[Training Custom Datasets with Ultralytics YOLOv8 in Google Colab](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab)**: Learn how to train custom datasets with Ultralytics YOLOv8 on Google Colab. This comprehensive blog post will take you through the entire process, from initial setup to the training and evaluation stages.
+- **[Training Custom Datasets with Ultralytics YOLO11 in Google Colab](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab)**: Learn how to train custom datasets with Ultralytics YOLO11 on Google Colab. This comprehensive blog post will take you through the entire process, from initial setup to the training and evaluation stages.
 
 - **[Curated Notebooks](https://colab.google/notebooks/)**: Here you can explore a series of organized and educational notebooks, each grouped by specific topic areas.
 
@@ -103,21 +103,21 @@ If you'd like to dive deeper into Google Colab, here are a few resources to guid
 
 ## Summary
 
-We've discussed how you can easily experiment with Ultralytics YOLOv8 models on Google Colab. You can use Google Colab to train and evaluate your models on GPUs and TPUs with a few clicks.
+We've discussed how you can easily experiment with Ultralytics YOLO11 models on Google Colab. You can use Google Colab to train and evaluate your models on GPUs and TPUs with a few clicks.
 
 For more details, visit [Google Colab's FAQ page](https://research.google.com/colaboratory/intl/en-GB/faq.html).
 
-Interested in more YOLOv8 integrations? Visit the [Ultralytics integration guide page](index.md) to explore additional tools and capabilities that can improve your machine-learning projects.
+Interested in more YOLO11 integrations? Visit the [Ultralytics integration guide page](index.md) to explore additional tools and capabilities that can improve your machine-learning projects.
 
 ## FAQ
 
-### How do I start training Ultralytics YOLOv8 models on Google Colab?
+### How do I start training Ultralytics YOLO11 models on Google Colab?
 
-To start training Ultralytics YOLOv8 models on Google Colab, sign in to your Google account, then access the [Google Colab YOLOv8 Notebook](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb). This notebook guides you through the setup and training process. After launching the notebook, run the cells step-by-step to train your model. For a full guide, refer to the [YOLOv8 Model Training guide](../modes/train.md).
+To start training Ultralytics YOLO11 models on Google Colab, sign in to your Google account, then access the [Google Colab YOLO11 Notebook](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb). This notebook guides you through the setup and training process. After launching the notebook, run the cells step-by-step to train your model. For a full guide, refer to the [YOLO11 Model Training guide](../modes/train.md).
 
-### What are the advantages of using Google Colab for training YOLOv8 models?
+### What are the advantages of using Google Colab for training YOLO11 models?
 
-Google Colab offers several advantages for training YOLOv8 models:
+Google Colab offers several advantages for training YOLO11 models:
 
 - **Zero Setup:** No initial environment setup is required; just log in and start coding.
 - **Free GPU Access:** Use powerful GPUs or TPUs without the need for expensive hardware.
@@ -126,7 +126,7 @@ Google Colab offers several advantages for training YOLOv8 models:
 
 For more information on why you should use Google Colab, explore the [training guide](../modes/train.md) and visit the [Google Colab page](https://colab.google/notebooks/).
 
-### How can I handle Google Colab session timeouts during YOLOv8 training?
+### How can I handle Google Colab session timeouts during YOLO11 training?
 
 Google Colab sessions timeout due to inactivity, especially for free users. To handle this:
 
@@ -136,9 +136,9 @@ Google Colab sessions timeout due to inactivity, especially for free users. To h
 
 For more tips on managing your Colab session, visit the [Google Colab FAQ page](https://research.google.com/colaboratory/intl/en-GB/faq.html).
 
-### Can I use custom datasets for training YOLOv8 models in Google Colab?
+### Can I use custom datasets for training YOLO11 models in Google Colab?
 
-Yes, you can use custom datasets to train YOLOv8 models in Google Colab. Upload your dataset to Google Drive and load it directly into your Colab notebook. You can follow Nicolai's YouTube guide, [How to Train YOLOv8 Models on Your Custom Dataset](https://www.youtube.com/watch?v=LNwODJXcvt4), or refer to the [Custom Dataset Training guide](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab) for detailed steps.
+Yes, you can use custom datasets to train YOLO11 models in Google Colab. Upload your dataset to Google Drive and load it directly into your Colab notebook. You can follow Nicolai's YouTube guide, [How to Train YOLO11 Models on Your Custom Dataset](https://www.youtube.com/watch?v=LNwODJXcvt4), or refer to the [Custom Dataset Training guide](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab) for detailed steps.
 
 ### What should I do if my Google Colab training session is interrupted?
 

docs/en/integrations/gradio.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Discover an interactive way to perform object detection with Ultralytics YOLOv8 using Gradio. Upload images and adjust settings for real-time results.
-keywords: Ultralytics, YOLOv8, Gradio, object detection, interactive, real-time, image processing, AI
+description: Discover an interactive way to perform object detection with Ultralytics YOLO11 using Gradio. Upload images and adjust settings for real-time results.
+keywords: Ultralytics, YOLO11, Gradio, object detection, interactive, real-time, image processing, AI
 ---
 
-# Interactive [Object Detection](https://www.ultralytics.com/glossary/object-detection): Gradio & Ultralytics YOLOv8 🚀
+# Interactive [Object Detection](https://www.ultralytics.com/glossary/object-detection): Gradio & Ultralytics YOLO11 🚀
 
 ## Introduction to Interactive Object Detection
 
-This Gradio interface provides an easy and interactive way to perform object detection using the [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) model. Users can upload images and adjust parameters like confidence threshold and intersection-over-union (IoU) threshold to get real-time detection results.
+This Gradio interface provides an easy and interactive way to perform object detection using the [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) model. Users can upload images and adjust parameters like confidence threshold and intersection-over-union (IoU) threshold to get real-time detection results.
 
 <p align="center">
   <br>
@@ -18,7 +18,7 @@ This Gradio interface provides an easy and interactive way to perform object det
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Gradio Integration with Ultralytics YOLOv8
+  <strong>Watch:</strong> Gradio Integration with Ultralytics YOLO11
 </p>
 
 ## Why Use Gradio for Object Detection?
@@ -52,7 +52,7 @@ pip install gradio
 
 ## Usage Example
 
-This section provides the Python code used to create the Gradio interface with the Ultralytics YOLOv8 model. Supports classification tasks, detection tasks, segmentation tasks, and key point tasks.
+This section provides the Python code used to create the Gradio interface with the Ultralytics YOLO11 model. Supports classification tasks, detection tasks, segmentation tasks, and key point tasks.
 
 ```python
 import gradio as gr
@@ -60,11 +60,11 @@ import PIL.Image as Image
 
 from ultralytics import ASSETS, YOLO
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 
 
 def predict_image(img, conf_threshold, iou_threshold):
-    """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
+    """Predicts objects in an image using a YOLO11 model with adjustable confidence and IOU thresholds."""
     results = model.predict(
         source=img,
         conf=conf_threshold,
@@ -90,7 +90,7 @@ iface = gr.Interface(
     ],
     outputs=gr.Image(type="pil", label="Result"),
     title="Ultralytics Gradio",
-    description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.",
+    description="Upload images for inference. The Ultralytics YOLO11n model is used by default.",
     examples=[
         [ASSETS / "bus.jpg", 0.25, 0.45],
         [ASSETS / "zidane.jpg", 0.25, 0.45],
@@ -119,9 +119,9 @@ if __name__ == "__main__":
 
 ## FAQ
 
-### How do I use Gradio with Ultralytics YOLOv8 for object detection?
+### How do I use Gradio with Ultralytics YOLO11 for object detection?
 
-To use Gradio with Ultralytics YOLOv8 for object detection, you can follow these steps:
+To use Gradio with Ultralytics YOLO11 for object detection, you can follow these steps:
 
 1. **Install Gradio:** Use the command `pip install gradio`.
 2. **Create Interface:** Write a Python script to initialize the Gradio interface. You can refer to the provided code example in the [documentation](#usage-example) for details.
@@ -134,7 +134,7 @@ import gradio as gr
 
 from ultralytics import YOLO
 
-model = YOLO("yolov8n.pt")
+model = YOLO("yolo11n.pt")
 
 
 def predict_image(img, conf_threshold, iou_threshold):
@@ -156,15 +156,15 @@ iface = gr.Interface(
         gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
     ],
     outputs=gr.Image(type="pil", label="Result"),
-    title="Ultralytics Gradio YOLOv8",
-    description="Upload images for YOLOv8 object detection.",
+    title="Ultralytics Gradio YOLO11",
+    description="Upload images for YOLO11 object detection.",
 )
 iface.launch()
 ```
 
-### What are the benefits of using Gradio for Ultralytics YOLOv8 object detection?
+### What are the benefits of using Gradio for Ultralytics YOLO11 object detection?
 
-Using Gradio for Ultralytics YOLOv8 object detection offers several benefits:
+Using Gradio for Ultralytics YOLO11 object detection offers several benefits:
 
 - **User-Friendly Interface:** Gradio provides an intuitive interface for users to upload images and visualize detection results without any coding effort.
 - **Real-Time Adjustments:** You can dynamically adjust detection parameters such as confidence and IoU thresholds and see the effects immediately.
@@ -172,22 +172,22 @@ Using Gradio for Ultralytics YOLOv8 object detection offers several benefits:
 
 For more details, you can read this [blog post](https://www.ultralytics.com/blog/ai-and-radiology-a-new-era-of-precision-and-efficiency).
 
-### Can I use Gradio and Ultralytics YOLOv8 together for educational purposes?
+### Can I use Gradio and Ultralytics YOLO11 together for educational purposes?
 
-Yes, Gradio and Ultralytics YOLOv8 can be utilized together for educational purposes effectively. Gradio's intuitive web interface makes it easy for students and educators to interact with state-of-the-art [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models like Ultralytics YOLOv8 without needing advanced programming skills. This setup is ideal for demonstrating key concepts in object detection and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), as Gradio provides immediate visual feedback which helps in understanding the impact of different parameters on the detection performance.
+Yes, Gradio and Ultralytics YOLO11 can be utilized together for educational purposes effectively. Gradio's intuitive web interface makes it easy for students and educators to interact with state-of-the-art [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models like Ultralytics YOLO11 without needing advanced programming skills. This setup is ideal for demonstrating key concepts in object detection and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), as Gradio provides immediate visual feedback which helps in understanding the impact of different parameters on the detection performance.
 
-### How do I adjust the confidence and IoU thresholds in the Gradio interface for YOLOv8?
+### How do I adjust the confidence and IoU thresholds in the Gradio interface for YOLO11?
 
-In the Gradio interface for YOLOv8, you can adjust the confidence and IoU thresholds using the sliders provided. These thresholds help control the prediction [accuracy](https://www.ultralytics.com/glossary/accuracy) and object separation:
+In the Gradio interface for YOLO11, you can adjust the confidence and IoU thresholds using the sliders provided. These thresholds help control the prediction [accuracy](https://www.ultralytics.com/glossary/accuracy) and object separation:
 
 - **Confidence Threshold:** Determines the minimum confidence level for detecting objects. Slide to increase or decrease the confidence required.
 - **IoU Threshold:** Sets the intersection-over-union threshold for distinguishing between overlapping objects. Adjust this value to refine object separation.
 
 For more information on these parameters, visit the [parameters explanation section](#parameters-explanation).
 
-### What are some practical applications of using Ultralytics YOLOv8 with Gradio?
+### What are some practical applications of using Ultralytics YOLO11 with Gradio?
 
-Practical applications of combining Ultralytics YOLOv8 with Gradio include:
+Practical applications of combining Ultralytics YOLO11 with Gradio include:
 
 - **Real-Time Object Detection Demonstrations:** Ideal for showcasing how object detection works in real-time.
 - **Educational Tools:** Useful in academic settings to teach object detection and computer vision concepts.
@@ -196,4 +196,4 @@ Practical applications of combining Ultralytics YOLOv8 with Gradio include:
 
 For examples of similar use cases, check out the [Ultralytics blog](https://www.ultralytics.com/blog/monitoring-animal-behavior-using-ultralytics-yolov8).
 
-Providing this information within the documentation will help in enhancing the usability and accessibility of Ultralytics YOLOv8, making it more approachable for users at all levels of expertise.
+Providing this information within the documentation will help in enhancing the usability and accessibility of Ultralytics YOLO11, making it more approachable for users at all levels of expertise.

docs/en/integrations/ibm-watsonx.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Dive into our detailed integration guide on using IBM Watson to train a YOLOv8 model. Uncover key features and step-by-step instructions on model training.
-keywords: IBM Watsonx, IBM Watsonx AI, What is Watson?, IBM Watson Integration, IBM Watson Features, YOLOv8, Ultralytics, Model Training, GPU, TPU, cloud computing
+description: Dive into our detailed integration guide on using IBM Watson to train a YOLO11 model. Uncover key features and step-by-step instructions on model training.
+keywords: IBM Watsonx, IBM Watsonx AI, What is Watson?, IBM Watson Integration, IBM Watson Features, YOLO11, Ultralytics, Model Training, GPU, TPU, cloud computing
 ---
 
-# A Step-by-Step Guide to Training YOLOv8 Models with IBM Watsonx
+# A Step-by-Step Guide to Training YOLO11 Models with IBM Watsonx
 
 Nowadays, scalable [computer vision solutions](../guides/steps-of-a-cv-project.md) are becoming more common and transforming the way we handle visual data. A great example is IBM Watsonx, an advanced AI and data platform that simplifies the development, deployment, and management of AI models. It offers a complete suite for the entire AI lifecycle and seamless integration with IBM Cloud services.
 
-You can train [Ultralytics YOLOv8 models](https://github.com/ultralytics/ultralytics) using IBM Watsonx. It's a good option for enterprises interested in efficient [model training](../modes/train.md), fine-tuning for specific tasks, and improving [model performance](../guides/model-evaluation-insights.md) with robust tools and a user-friendly setup. In this guide, we'll walk you through the process of training YOLOv8 with IBM Watsonx, covering everything from setting up your environment to evaluating your trained models. Let's get started!
+You can train [Ultralytics YOLO11 models](https://github.com/ultralytics/ultralytics) using IBM Watsonx. It's a good option for enterprises interested in efficient [model training](../modes/train.md), fine-tuning for specific tasks, and improving [model performance](../guides/model-evaluation-insights.md) with robust tools and a user-friendly setup. In this guide, we'll walk you through the process of training YOLO11 with IBM Watsonx, covering everything from setting up your environment to evaluating your trained models. Let's get started!
 
 ## What is IBM Watsonx?
 
@@ -36,9 +36,9 @@ Watsonx.data supports both cloud and on-premises deployments through the IBM Sto
 
 Watsonx.governance makes compliance easier by automatically identifying regulatory changes and enforcing policies. It links requirements to internal risk data and provides up-to-date AI factsheets. The platform helps manage risk with alerts and tools to detect issues such as [bias and drift](../guides/model-monitoring-and-maintenance.md). It also automates the monitoring and documentation of the AI lifecycle, organizes AI development with a model inventory, and enhances collaboration with user-friendly dashboards and reporting tools.
 
-## How to Train YOLOv8 Using IBM Watsonx
+## How to Train YOLO11 Using IBM Watsonx
 
-You can use IBM Watsonx to accelerate your YOLOv8 model training workflow.
+You can use IBM Watsonx to accelerate your YOLO11 model training workflow.
 
 ### Prerequisites
 
@@ -67,7 +67,7 @@ Next, you can install and import the necessary Python libraries.
         pip install ultralytics==8.0.196
         ```
 
-For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
+For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
 
 Then, you can import the needed packages.
 
@@ -86,7 +86,7 @@ Then, you can import the needed packages.
 
 ### Step 3: Load the Data
 
-For this tutorial, we will use a [marine litter dataset](https://www.kaggle.com/datasets/atiqishrak/trash-dataset-icra19) available on Kaggle. With this dataset, we will custom-train a YOLOv8 model to detect and classify litter and biological objects in underwater images.
+For this tutorial, we will use a [marine litter dataset](https://www.kaggle.com/datasets/atiqishrak/trash-dataset-icra19) available on Kaggle. With this dataset, we will custom-train a YOLO11 model to detect and classify litter and biological objects in underwater images.
 
 We can load the dataset directly into the notebook using the Kaggle API. First, create a free Kaggle account. Once you have created an account, you'll need to generate an API key. Directions for generating your key can be found in the [Kaggle API documentation](https://github.com/Kaggle/kaggle-api/blob/main/docs/README.md) under the section "API credentials".
 
@@ -236,34 +236,34 @@ Run the following script to delete the current contents of config.yaml and repla
             print(f"{file_path} updated successfully.")
         ```
 
-### Step 5: Train the YOLOv8 model
+### Step 5: Train the YOLO11 model
 
-Run the following command-line code to fine tune a pretrained default YOLOv8 model.
+Run the following command-line code to fine tune a pretrained default YOLO11 model.
 
-!!! example "Train the YOLOv8 model"
+!!! example "Train the YOLO11 model"
 
     === "CLI"
 
         ```bash
-        !yolo task=detect mode=train data={work_dir}/trash_ICRA19/config.yaml model=yolov8s.pt epochs=2 batch=32 lr0=.04 plots=True
+        !yolo task=detect mode=train data={work_dir}/trash_ICRA19/config.yaml model=yolo11n.pt epochs=2 batch=32 lr0=.04 plots=True
         ```
 
 Here's a closer look at the parameters in the model training command:
 
 - **task**: It specifies the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) task for which you are using the specified YOLO model and data set.
 - **mode**: Denotes the purpose for which you are loading the specified model and data. Since we are training a model, it is set to "train." Later, when we test our model's performance, we will set it to "predict."
-- **epochs**: This delimits the number of times YOLOv8 will pass through our entire data set.
+- **epochs**: This delimits the number of times YOLO11 will pass through our entire data set.
 - **batch**: The numerical value stipulates the training [batch sizes](https://www.ultralytics.com/glossary/batch-size). Batches are the number of images a model processes before it updates its parameters.
 - **lr0**: Specifies the model's initial [learning rate](https://www.ultralytics.com/glossary/learning-rate).
 - **plots**: Directs YOLO to generate and save plots of our model's training and evaluation metrics.
 
-For a detailed understanding of the model training process and best practices, refer to the [YOLOv8 Model Training guide](../modes/train.md). This guide will help you get the most out of your experiments and ensure you're using YOLOv8 effectively.
+For a detailed understanding of the model training process and best practices, refer to the [YOLO11 Model Training guide](../modes/train.md). This guide will help you get the most out of your experiments and ensure you're using YOLO11 effectively.
 
 ### Step 6: Test the Model
 
 We can now run inference to test the performance of our fine-tuned model:
 
-!!! example "Test the YOLOv8 model"
+!!! example "Test the YOLO11 model"
 
     === "CLI"
 
@@ -312,11 +312,11 @@ Unlike precision, recall moves in the opposite direction, showing greater recall
 
 ### Step 8: Calculating [Intersection Over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou)
 
-You can measure the prediction [accuracy](https://www.ultralytics.com/glossary/accuracy) by calculating the IoU between a predicted bounding box and a ground truth bounding box for the same object. Check out [IBM's tutorial on training YOLOv8](https://developer.ibm.com/tutorials/awb-train-yolo-object-detection-model-in-python/) for more details.
+You can measure the prediction [accuracy](https://www.ultralytics.com/glossary/accuracy) by calculating the IoU between a predicted bounding box and a ground truth bounding box for the same object. Check out [IBM's tutorial on training YOLO11](https://developer.ibm.com/tutorials/awb-train-yolo-object-detection-model-in-python/) for more details.
 
 ## Summary
 
-We explored IBM Watsonx key features, and how to train a YOLOv8 model using IBM Watsonx. We also saw how IBM Watsonx can enhance your AI workflows with advanced tools for model building, data management, and compliance.
+We explored IBM Watsonx key features, and how to train a YOLO11 model using IBM Watsonx. We also saw how IBM Watsonx can enhance your AI workflows with advanced tools for model building, data management, and compliance.
 
 For further details on usage, visit [IBM Watsonx official documentation](https://www.ibm.com/watsonx).
 
@@ -324,9 +324,9 @@ Also, be sure to check out the [Ultralytics integration guide page](./index.md),
 
 ## FAQ
 
-### How do I train a YOLOv8 model using IBM Watsonx?
+### How do I train a YOLO11 model using IBM Watsonx?
 
-To train a YOLOv8 model using IBM Watsonx, follow these steps:
+To train a YOLO11 model using IBM Watsonx, follow these steps:
 
 1. **Set Up Your Environment**: Create an IBM Cloud account and set up a Watsonx.ai project. Use a Jupyter Notebook for your coding environment.
 2. **Install Libraries**: Install necessary libraries like `torch`, `opencv`, and `ultralytics`.
@@ -335,7 +335,7 @@ To train a YOLOv8 model using IBM Watsonx, follow these steps:
 5. **Train the Model**: Use the YOLO command-line interface to train your model with specific parameters like `epochs`, `batch size`, and `learning rate`.
 6. **Test and Evaluate**: Run inference to test the model and evaluate its performance using metrics like precision and recall.
 
-For detailed instructions, refer to our [YOLOv8 Model Training guide](../modes/train.md).
+For detailed instructions, refer to our [YOLO11 Model Training guide](../modes/train.md).
 
 ### What are the key features of IBM Watsonx for AI model training?
 
@@ -347,20 +347,20 @@ IBM Watsonx offers several key features for AI model training:
 
 For more information, visit the [IBM Watsonx official documentation](https://www.ibm.com/watsonx).
 
-### Why should I use IBM Watsonx for training Ultralytics YOLOv8 models?
+### Why should I use IBM Watsonx for training Ultralytics YOLO11 models?
 
-IBM Watsonx is an excellent choice for training Ultralytics YOLOv8 models due to its comprehensive suite of tools that streamline the AI lifecycle. Key benefits include:
+IBM Watsonx is an excellent choice for training Ultralytics YOLO11 models due to its comprehensive suite of tools that streamline the AI lifecycle. Key benefits include:
 
 - **Scalability**: Easily scale your model training with IBM Cloud services.
 - **Integration**: Seamlessly integrate with various data sources and APIs.
 - **User-Friendly Interface**: Simplifies the development process with a collaborative and intuitive interface.
 - **Advanced Tools**: Access to powerful tools like the Prompt Lab, Tuning Studio, and Flows Engine for enhancing model performance.
 
-Learn more about [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) and how to train models using IBM Watsonx in our [integration guide](./index.md).
+Learn more about [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) and how to train models using IBM Watsonx in our [integration guide](./index.md).
 
-### How can I preprocess my dataset for YOLOv8 training on IBM Watsonx?
+### How can I preprocess my dataset for YOLO11 training on IBM Watsonx?
 
-To preprocess your dataset for YOLOv8 training on IBM Watsonx:
+To preprocess your dataset for YOLO11 training on IBM Watsonx:
 
 1. **Organize Directories**: Ensure your dataset follows the YOLO directory structure with separate subdirectories for images and labels within the train/val/test split.
 2. **Update .yaml File**: Modify the `.yaml` configuration file to reflect the new directory structure and class names.
@@ -399,9 +399,9 @@ if __name__ == "__main__":
 
 For more details, refer to our [data preprocessing guide](../guides/preprocessing_annotated_data.md).
 
-### What are the prerequisites for training a YOLOv8 model on IBM Watsonx?
+### What are the prerequisites for training a YOLO11 model on IBM Watsonx?
 
-Before you start training a YOLOv8 model on IBM Watsonx, ensure you have the following prerequisites:
+Before you start training a YOLO11 model on IBM Watsonx, ensure you have the following prerequisites:
 
 - **IBM Cloud Account**: Create an account on IBM Cloud to access Watsonx.ai.
 - **Kaggle Account**: For loading datasets, you'll need a Kaggle account and an API key.

docs/en/integrations/index.md

@@ -18,7 +18,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Ultralytics YOLOv8 Deployment and Integrations
+  <strong>Watch:</strong> Ultralytics YOLO11 Deployment and Integrations
 </p>
 
 ## Datasets Integrations
@@ -47,7 +47,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
 
 - [Amazon SageMaker](amazon-sagemaker.md): Leverage Amazon SageMaker to efficiently build, train, and deploy Ultralytics models, providing an all-in-one platform for the ML lifecycle.
 
-- [Paperspace Gradient](paperspace.md): Paperspace Gradient simplifies working on YOLOv8 projects by providing easy-to-use cloud tools for training, testing, and deploying your models quickly.
+- [Paperspace Gradient](paperspace.md): Paperspace Gradient simplifies working on YOLO11 projects by providing easy-to-use cloud tools for training, testing, and deploying your models quickly.
 
 - [Google Colab](google-colab.md): Use Google Colab to train and evaluate Ultralytics models in a cloud-based environment that supports collaboration and sharing.
 
@@ -111,7 +111,7 @@ Let's collaborate to make the Ultralytics YOLO ecosystem more expansive and feat
 
 ### What is Ultralytics HUB, and how does it streamline the ML workflow?
 
-Ultralytics HUB is a cloud-based platform designed to make machine learning (ML) workflows for Ultralytics models seamless and efficient. By using this tool, you can easily upload datasets, train models, perform real-time tracking, and deploy YOLOv8 models without needing extensive coding skills. You can explore the key features on the [Ultralytics HUB](https://hub.ultralytics.com/) page and get started quickly with our [Quickstart](https://docs.ultralytics.com/hub/quickstart/) guide.
+Ultralytics HUB is a cloud-based platform designed to make machine learning (ML) workflows for Ultralytics models seamless and efficient. By using this tool, you can easily upload datasets, train models, perform real-time tracking, and deploy YOLO11 models without needing extensive coding skills. You can explore the key features on the [Ultralytics HUB](https://hub.ultralytics.com/) page and get started quickly with our [Quickstart](https://docs.ultralytics.com/hub/quickstart/) guide.
 
 ### How do I integrate Ultralytics YOLO models with Roboflow for dataset management?
 
@@ -121,9 +121,9 @@ Integrating Ultralytics YOLO models with Roboflow enhances dataset management by
 
 Yes, you can. Integrating MLFlow with Ultralytics models allows you to track experiments, improve reproducibility, and streamline the entire ML lifecycle. Detailed instructions for setting up this integration can be found on the [MLFlow](mlflow.md) integration page. This integration is particularly useful for monitoring model metrics and managing the ML workflow efficiently.
 
-### What are the benefits of using Neural Magic for YOLOv8 model optimization?
+### What are the benefits of using Neural Magic for YOLO11 model optimization?
 
-Neural Magic optimizes YOLOv8 models by leveraging techniques like Quantization Aware Training (QAT) and pruning, resulting in highly efficient, smaller models that perform better on resource-limited hardware. Check out the [Neural Magic](neural-magic.md) integration page to learn how to implement these optimizations for superior performance and leaner models. This is especially beneficial for deployment on edge devices.
+Neural Magic optimizes YOLO11 models by leveraging techniques like Quantization Aware Training (QAT) and pruning, resulting in highly efficient, smaller models that perform better on resource-limited hardware. Check out the [Neural Magic](neural-magic.md) integration page to learn how to implement these optimizations for superior performance and leaner models. This is especially beneficial for deployment on edge devices.
 
 ### How do I deploy Ultralytics YOLO models with Gradio for interactive demos?
 

docs/en/integrations/jupyterlab.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Explore our integration guide that explains how you can use JupyterLab to train a YOLOv8 model. We'll also cover key features and tips for common issues.
-keywords: JupyterLab, What is JupyterLab, How to Use JupyterLab, JupyterLab How to Use, YOLOv8, Ultralytics, Model Training, GPU, TPU, cloud computing
+description: Explore our integration guide that explains how you can use JupyterLab to train a YOLO11 model. We'll also cover key features and tips for common issues.
+keywords: JupyterLab, What is JupyterLab, How to Use JupyterLab, JupyterLab How to Use, YOLO11, Ultralytics, Model Training, GPU, TPU, cloud computing
 ---
 
-# A Guide on How to Use JupyterLab to Train Your YOLOv8 Models
+# A Guide on How to Use JupyterLab to Train Your YOLO11 Models
 
 Building [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models can be tough, especially when you don't have the right tools or environment to work with. If you are facing this issue, JupyterLab might be the right solution for you. JupyterLab is a user-friendly, web-based platform that makes coding more flexible and interactive. You can use it to handle big datasets, create complex models, and even collaborate with others, all in one place.
 
-You can use JupyterLab to [work on projects](../guides/steps-of-a-cv-project.md) related to [Ultralytics YOLOv8 models](https://github.com/ultralytics/ultralytics). JupyterLab is a great option for efficient model development and experimentation. It makes it easy to start experimenting with and [training YOLOv8 models](../modes/train.md) right from your computer. Let's dive deeper into JupyterLab, its key features, and how you can use it to train YOLOv8 models.
+You can use JupyterLab to [work on projects](../guides/steps-of-a-cv-project.md) related to [Ultralytics YOLO11 models](https://github.com/ultralytics/ultralytics). JupyterLab is a great option for efficient model development and experimentation. It makes it easy to start experimenting with and [training YOLO11 models](../modes/train.md) right from your computer. Let's dive deeper into JupyterLab, its key features, and how you can use it to train YOLO11 models.
 
 ## What is JupyterLab?
 
@@ -26,7 +26,7 @@ Here are some of the key features that make JupyterLab a great option for model
 - **Markdown Preview**: Working with Markdown files is more efficient in JupyterLab, thanks to its simultaneous preview feature. As you write or edit your Markdown file, you can see the formatted output in real-time. It makes it easier to double-check that your documentation looks perfect, saving you from having to switch back and forth between editing and preview modes.
 - **Run Code from Text Files**: If you're sharing a text file with code, JupyterLab makes it easy to run it directly within the platform. You can highlight the code and press Shift + Enter to execute it. It is great for verifying code snippets quickly and helps guarantee that the code you share is functional and error-free.
 
-## Why Should You Use JupyterLab for Your YOLOv8 Projects?
+## Why Should You Use JupyterLab for Your YOLO11 Projects?
 
 There are multiple platforms for developing and evaluating machine learning models, so what makes JupyterLab stand out? Let's explore some of the unique aspects that JupyterLab offers for your machine-learning projects:
 
@@ -46,9 +46,9 @@ When working with Kaggle, you might come across some common issues. Here are som
 - **Installing JupyterLab Extensions**: JupyterLab supports various extensions to enhance functionality. You can install and customize these extensions to suit your needs. For detailed instructions, refer to [JupyterLab Extensions Guide](https://jupyterlab.readthedocs.io/en/latest/user/extensions.html) for more information.
 - **Using Multiple Versions of Python**: If you need to work with different versions of Python, you can use Jupyter kernels configured with different Python versions.
 
-## How to Use JupyterLab to Try Out YOLOv8
+## How to Use JupyterLab to Try Out YOLO11
 
-JupyterLab makes it easy to experiment with YOLOv8. To get started, follow these simple steps.
+JupyterLab makes it easy to experiment with YOLO11. To get started, follow these simple steps.
 
 ### Step 1: Install JupyterLab
 
@@ -63,7 +63,7 @@ First, you need to install JupyterLab. Open your terminal and run the command:
         pip install jupyterlab
         ```
 
-### Step 2: Download the YOLOv8 Tutorial Notebook
+### Step 2: Download the YOLO11 Tutorial Notebook
 
 Next, download the [tutorial.ipynb](https://github.com/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb) file from the Ultralytics GitHub repository. Save this file to any directory on your local machine.
 
@@ -85,13 +85,13 @@ Once you've run this command, it will open JupyterLab in your default web browse
 
 ### Step 4: Start Experimenting
 
-In JupyterLab, open the tutorial.ipynb notebook. You can now start running the cells to explore and experiment with YOLOv8.
+In JupyterLab, open the tutorial.ipynb notebook. You can now start running the cells to explore and experiment with YOLO11.
 
-![Image Showing Opened YOLOv8 Notebook in JupyterLab](https://github.com/ultralytics/docs/releases/download/0/opened-yolov8-notebook-jupyterlab.avif)
+![Image Showing Opened YOLO11 Notebook in JupyterLab](https://github.com/ultralytics/docs/releases/download/0/opened-yolov8-notebook-jupyterlab.avif)
 
-JupyterLab's interactive environment allows you to modify code, visualize outputs, and document your findings all in one place. You can try out different configurations and understand how YOLOv8 works.
+JupyterLab's interactive environment allows you to modify code, visualize outputs, and document your findings all in one place. You can try out different configurations and understand how YOLO11 works.
 
-For a detailed understanding of the model training process and best practices, refer to the [YOLOv8 Model Training guide](../modes/train.md). This guide will help you get the most out of your experiments and ensure you're using YOLOv8 effectively.
+For a detailed understanding of the model training process and best practices, refer to the [YOLO11 Model Training guide](../modes/train.md). This guide will help you get the most out of your experiments and ensure you're using YOLO11 effectively.
 
 ## Keep Learning about Jupyterlab
 
@@ -103,17 +103,17 @@ If you're excited to learn more about JupyterLab, here are some great resources
 
 ## Summary
 
-We've explored how JupyterLab can be a powerful tool for experimenting with Ultralytics YOLOv8 models. Using its flexible and interactive environment, you can easily set up JupyterLab on your local machine and start working with YOLOv8. JupyterLab makes it simple to [train](../guides/model-training-tips.md) and [evaluate](../guides/model-testing.md) your models, visualize outputs, and [document your findings](../guides/model-monitoring-and-maintenance.md) all in one place.
+We've explored how JupyterLab can be a powerful tool for experimenting with Ultralytics YOLO11 models. Using its flexible and interactive environment, you can easily set up JupyterLab on your local machine and start working with YOLO11. JupyterLab makes it simple to [train](../guides/model-training-tips.md) and [evaluate](../guides/model-testing.md) your models, visualize outputs, and [document your findings](../guides/model-monitoring-and-maintenance.md) all in one place.
 
 For more details, visit the [JupyterLab FAQ Page](https://jupyterlab.readthedocs.io/en/stable/getting_started/faq.html).
 
-Interested in more YOLOv8 integrations? Check out the [Ultralytics integration guide](./index.md) to explore additional tools and capabilities for your machine learning projects.
+Interested in more YOLO11 integrations? Check out the [Ultralytics integration guide](./index.md) to explore additional tools and capabilities for your machine learning projects.
 
 ## FAQ
 
-### How do I use JupyterLab to train a YOLOv8 model?
+### How do I use JupyterLab to train a YOLO11 model?
 
-To train a YOLOv8 model using JupyterLab:
+To train a YOLO11 model using JupyterLab:
 
 1. Install JupyterLab and the Ultralytics package:
 
@@ -128,7 +128,7 @@ To train a YOLOv8 model using JupyterLab:
     ```python
     from ultralytics import YOLO
 
-    model = YOLO("yolov8n.pt")
+    model = YOLO("yolo11n.pt")
     ```
 
 4. Train the model on your custom dataset:
@@ -147,22 +147,22 @@ To train a YOLOv8 model using JupyterLab:
 
 JupyterLab's interactive environment allows you to easily modify parameters, visualize results, and iterate on your model training process.
 
-### What are the key features of JupyterLab that make it suitable for YOLOv8 projects?
+### What are the key features of JupyterLab that make it suitable for YOLO11 projects?
 
-JupyterLab offers several features that make it ideal for YOLOv8 projects:
+JupyterLab offers several features that make it ideal for YOLO11 projects:
 
-1. Interactive code execution: Test and debug YOLOv8 code snippets in real-time.
+1. Interactive code execution: Test and debug YOLO11 code snippets in real-time.
 2. Integrated file browser: Easily manage datasets, model weights, and configuration files.
 3. Flexible layout: Arrange multiple notebooks, terminals, and output windows side-by-side for efficient workflow.
-4. Rich output display: Visualize YOLOv8 detection results, training curves, and model performance metrics inline.
-5. Markdown support: Document your YOLOv8 experiments and findings with rich text and images.
+4. Rich output display: Visualize YOLO11 detection results, training curves, and model performance metrics inline.
+5. Markdown support: Document your YOLO11 experiments and findings with rich text and images.
 6. Extension ecosystem: Enhance functionality with extensions for version control, [remote computing](google-colab.md), and more.
 
-These features allow for a seamless development experience when working with YOLOv8 models, from data preparation to [model deployment](https://www.ultralytics.com/glossary/model-deployment).
+These features allow for a seamless development experience when working with YOLO11 models, from data preparation to [model deployment](https://www.ultralytics.com/glossary/model-deployment).
 
-### How can I optimize YOLOv8 model performance using JupyterLab?
+### How can I optimize YOLO11 model performance using JupyterLab?
 
-To optimize YOLOv8 model performance in JupyterLab:
+To optimize YOLO11 model performance in JupyterLab:
 
 1. Use the autobatch feature to determine the optimal batch size:
 
@@ -190,11 +190,11 @@ To optimize YOLOv8 model performance in JupyterLab:
 
 4. Experiment with different model architectures and [export formats](../modes/export.md) to find the best balance of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for your specific use case.
 
-JupyterLab's interactive environment allows for quick iterations and real-time feedback, making it easier to optimize your YOLOv8 models efficiently.
+JupyterLab's interactive environment allows for quick iterations and real-time feedback, making it easier to optimize your YOLO11 models efficiently.
 
-### How do I handle common issues when working with JupyterLab and YOLOv8?
+### How do I handle common issues when working with JupyterLab and YOLO11?
 
-When working with JupyterLab and YOLOv8, you might encounter some common issues. Here's how to handle them:
+When working with JupyterLab and YOLO11, you might encounter some common issues. Here's how to handle them:
 
 1. GPU memory issues:
 
@@ -203,7 +203,7 @@ When working with JupyterLab and YOLOv8, you might encounter some common issues.
 
 2. Package conflicts:
 
-    - Create a separate conda environment for your YOLOv8 projects to avoid conflicts.
+    - Create a separate conda environment for your YOLO11 projects to avoid conflicts.
     - Use `!pip install package_name` in a notebook cell to install missing packages.
 
 3. Kernel crashes:

docs/en/integrations/kaggle.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Dive into our guide on YOLOv8's integration with Kaggle. Find out what Kaggle is, its key features, and how to train a YOLOv8 model using the integration.
-keywords: What is Kaggle, What is Kaggle Used For, YOLOv8, Kaggle Machine Learning, Model Training, GPU, TPU, cloud computing
+description: Dive into our guide on YOLO11's integration with Kaggle. Find out what Kaggle is, its key features, and how to train a YOLO11 model using the integration.
+keywords: What is Kaggle, What is Kaggle Used For, YOLO11, Kaggle Machine Learning, Model Training, GPU, TPU, cloud computing
 ---
 
-# A Guide on Using Kaggle to Train Your YOLOv8 Models
+# A Guide on Using Kaggle to Train Your YOLO11 Models
 
 If you are learning about AI and working on [small projects](../solutions/index.md), you might not have access to powerful computing resources yet, and high-end hardware can be pretty expensive. Fortunately, Kaggle, a platform owned by Google, offers a great solution. Kaggle provides a free, cloud-based environment where you can access GPU resources, handle large datasets, and collaborate with a diverse community of data scientists and [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) enthusiasts.
 
-Kaggle is a great choice for [training](../guides/model-training-tips.md) and experimenting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics?tab=readme-ov-file) models. Kaggle Notebooks make using popular machine-learning libraries and frameworks in your projects easy. Let's explore Kaggle's main features and learn how you can train YOLOv8 models on this platform!
+Kaggle is a great choice for [training](../guides/model-training-tips.md) and experimenting with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics?tab=readme-ov-file) models. Kaggle Notebooks make using popular machine-learning libraries and frameworks in your projects easy. Let's explore Kaggle's main features and learn how you can train YOLO11 models on this platform!
 
 ## What is Kaggle?
 
@@ -16,21 +16,21 @@ Kaggle is a platform that brings together data scientists from around the world
 
 With more than [10 million users](https://www.kaggle.com/discussions/general/332147) as of 2022, Kaggle provides a rich environment for developing and experimenting with machine learning models. You don't need to worry about your local machine's specs or setup; you can dive right in with just a Kaggle account and a web browser.
 
-## Training YOLOv8 Using Kaggle
+## Training YOLO11 Using Kaggle
 
-Training YOLOv8 models on Kaggle is simple and efficient, thanks to the platform's access to powerful GPUs.
+Training YOLO11 models on Kaggle is simple and efficient, thanks to the platform's access to powerful GPUs.
 
-To get started, access the [Kaggle YOLOv8 Notebook](https://www.kaggle.com/code/ultralytics/yolov8). Kaggle's environment comes with pre-installed libraries like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making the setup process hassle-free.
+To get started, access the [Kaggle YOLO11 Notebook](https://www.kaggle.com/code/ultralytics/yolov8). Kaggle's environment comes with pre-installed libraries like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making the setup process hassle-free.
 
-![What is the kaggle integration with respect to YOLOv8?](https://github.com/ultralytics/docs/releases/download/0/kaggle-integration-yolov8.avif)
+![What is the kaggle integration with respect to YOLO11?](https://github.com/ultralytics/docs/releases/download/0/kaggle-integration-yolov8.avif)
 
-Once you sign in to your Kaggle account, you can click on the option to copy and edit the code, select a GPU under the accelerator settings, and run the notebook's cells to begin training your model. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
+Once you sign in to your Kaggle account, you can click on the option to copy and edit the code, select a GPU under the accelerator settings, and run the notebook's cells to begin training your model. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
 
 ![Using kaggle for machine learning model training with a GPU](https://github.com/ultralytics/docs/releases/download/0/using-kaggle-for-machine-learning-model-training-with-a-gpu.avif)
 
-On the [official YOLOv8 Kaggle notebook page](https://www.kaggle.com/code/ultralytics/yolov8), if you click on the three dots in the upper right-hand corner, you'll notice more options will pop up.
+On the [official YOLO11 Kaggle notebook page](https://www.kaggle.com/code/ultralytics/yolov8), if you click on the three dots in the upper right-hand corner, you'll notice more options will pop up.
 
-![Overview of Options From the Official YOLOv8 Kaggle Notebook Page](https://github.com/ultralytics/docs/releases/download/0/overview-options-yolov8-kaggle-notebook.avif)
+![Overview of Options From the Official YOLO11 Kaggle Notebook Page](https://github.com/ultralytics/docs/releases/download/0/overview-options-yolov8-kaggle-notebook.avif)
 
 These options include:
 
@@ -59,17 +59,17 @@ When working with Kaggle, you might come across some common issues. Here are som
 
 Next, let's understand the features Kaggle offers that make it an excellent platform for data science and machine learning enthusiasts. Here are some of the key highlights:
 
-- **Datasets**: Kaggle hosts a massive collection of datasets on various topics. You can easily search and use these datasets in your projects, which is particularly handy for training and testing your YOLOv8 models.
+- **Datasets**: Kaggle hosts a massive collection of datasets on various topics. You can easily search and use these datasets in your projects, which is particularly handy for training and testing your YOLO11 models.
 - **Competitions**: Known for its exciting competitions, Kaggle allows data scientists and machine learning enthusiasts to solve real-world problems. Competing helps you improve your skills, learn new techniques, and gain recognition in the community.
-- **Free Access to TPUs**: Kaggle provides free access to powerful TPUs, which are essential for training complex machine learning models. This means you can speed up processing and boost the performance of your YOLOv8 projects without incurring extra costs.
+- **Free Access to TPUs**: Kaggle provides free access to powerful TPUs, which are essential for training complex machine learning models. This means you can speed up processing and boost the performance of your YOLO11 projects without incurring extra costs.
 - **Integration with Github**: Kaggle allows you to easily connect your GitHub repository to upload notebooks and save your work. This integration makes it convenient to manage and access your files.
 - **Community and Discussions**: Kaggle boasts a strong community of data scientists and machine learning practitioners. The discussion forums and shared notebooks are fantastic resources for learning and troubleshooting. You can easily find help, share your knowledge, and collaborate with others.
 
-## Why Should You Use Kaggle for Your YOLOv8 Projects?
+## Why Should You Use Kaggle for Your YOLO11 Projects?
 
 There are multiple platforms for training and evaluating machine learning models, so what makes Kaggle stand out? Let's dive into the benefits of using Kaggle for your machine-learning projects:
 
-- **Public Notebooks**: You can make your Kaggle notebooks public, allowing other users to view, vote, fork, and discuss your work. Kaggle promotes collaboration, feedback, and the sharing of ideas, helping you improve your YOLOv8 models.
+- **Public Notebooks**: You can make your Kaggle notebooks public, allowing other users to view, vote, fork, and discuss your work. Kaggle promotes collaboration, feedback, and the sharing of ideas, helping you improve your YOLO11 models.
 - **Comprehensive History of Notebook Commits**: Kaggle creates a detailed history of your notebook commits. This allows you to review and track changes over time, making it easier to understand the evolution of your project and revert to previous versions if needed.
 - **Console Access**: Kaggle provides a console, giving you more control over your environment. This feature allows you to perform various tasks directly from the command line, enhancing your workflow and productivity.
 - **Resource Availability**: Each notebook editing session on Kaggle is provided with significant resources: 12 hours of execution time for CPU and GPU sessions, 9 hours of execution time for TPU sessions, and 20 gigabytes of auto-saved disk space.
@@ -85,21 +85,21 @@ If you want to learn more about Kaggle, here are some helpful resources to guide
 
 ## Summary
 
-We've seen how Kaggle can boost your YOLOv8 projects by providing free access to powerful GPUs, making model training and evaluation efficient. Kaggle's platform is user-friendly, with pre-installed libraries for quick setup.
+We've seen how Kaggle can boost your YOLO11 projects by providing free access to powerful GPUs, making model training and evaluation efficient. Kaggle's platform is user-friendly, with pre-installed libraries for quick setup.
 
 For more details, visit [Kaggle's documentation](https://www.kaggle.com/docs).
 
-Interested in more YOLOv8 integrations? Check out the[ Ultralytics integration guide](https://docs.ultralytics.com/integrations/) to explore additional tools and capabilities for your machine learning projects.
+Interested in more YOLO11 integrations? Check out the[ Ultralytics integration guide](https://docs.ultralytics.com/integrations/) to explore additional tools and capabilities for your machine learning projects.
 
 ## FAQ
 
-### How do I train a YOLOv8 model on Kaggle?
+### How do I train a YOLO11 model on Kaggle?
 
-Training a YOLOv8 model on Kaggle is straightforward. First, access the [Kaggle YOLOv8 Notebook](https://www.kaggle.com/ultralytics/yolov8). Sign in to your Kaggle account, copy and edit the notebook, and select a GPU under the accelerator settings. Run the notebook cells to start training. For more detailed steps, refer to our [YOLOv8 Model Training guide](../modes/train.md).
+Training a YOLO11 model on Kaggle is straightforward. First, access the [Kaggle YOLO11 Notebook](https://www.kaggle.com/ultralytics/yolov8). Sign in to your Kaggle account, copy and edit the notebook, and select a GPU under the accelerator settings. Run the notebook cells to start training. For more detailed steps, refer to our [YOLO11 Model Training guide](../modes/train.md).
 
-### What are the benefits of using Kaggle for YOLOv8 model training?
+### What are the benefits of using Kaggle for YOLO11 model training?
 
-Kaggle offers several advantages for training YOLOv8 models:
+Kaggle offers several advantages for training YOLO11 models:
 
 - **Free GPU Access**: Utilize powerful GPUs like Nvidia Tesla P100 or T4 x2 for up to 30 hours per week.
 - **Pre-installed Libraries**: Libraries like TensorFlow and PyTorch are pre-installed, simplifying the setup.
@@ -108,7 +108,7 @@ Kaggle offers several advantages for training YOLOv8 models:
 
 For more details, visit our [Ultralytics integration guide](https://docs.ultralytics.com/integrations/).
 
-### What common issues might I encounter when using Kaggle for YOLOv8, and how can I resolve them?
+### What common issues might I encounter when using Kaggle for YOLO11, and how can I resolve them?
 
 Common issues include:
 
@@ -119,7 +119,7 @@ Common issues include:
 
 For more troubleshooting tips, see our [Common Issues guide](../guides/yolo-common-issues.md).
 
-### Why should I choose Kaggle over other platforms like Google Colab for training YOLOv8 models?
+### Why should I choose Kaggle over other platforms like Google Colab for training YOLO11 models?
 
 Kaggle offers unique features that make it an excellent choice:
 

docs/en/integrations/ncnn.md

@@ -1,14 +1,14 @@
 ---
 comments: true
-description: Optimize YOLOv8 models for mobile and embedded devices by exporting to NCNN format. Enhance performance in resource-constrained environments.
-keywords: Ultralytics, YOLOv8, NCNN, model export, machine learning, deployment, mobile, embedded systems, deep learning, AI models
+description: Optimize YOLO11 models for mobile and embedded devices by exporting to NCNN format. Enhance performance in resource-constrained environments.
+keywords: Ultralytics, YOLO11, NCNN, model export, machine learning, deployment, mobile, embedded systems, deep learning, AI models
 ---
 
-# How to Export to NCNN from YOLOv8 for Smooth Deployment
+# How to Export to NCNN from YOLO11 for Smooth Deployment
 
 Deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models on devices with limited computational power, such as mobile or embedded systems, can be tricky. You need to make sure you use a format optimized for optimal performance. This makes sure that even devices with limited processing power can handle advanced computer vision tasks well.
 
-The export to NCNN format feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for lightweight device-based applications. In this guide, we'll walk you through how to convert your models to the NCNN format, making it easier for your models to perform well on various mobile and embedded devices.
+The export to NCNN format feature allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for lightweight device-based applications. In this guide, we'll walk you through how to convert your models to the NCNN format, making it easier for your models to perform well on various mobile and embedded devices.
 
 ## Why should you export to NCNN?
 
@@ -34,7 +34,7 @@ NCNN models offer a wide range of key features that enable on-device [machine le
 
 ## Deployment Options with NCNN
 
-Before we look at the code for exporting YOLOv8 models to the NCNN format, let's understand how NCNN models are normally used.
+Before we look at the code for exporting YOLO11 models to the NCNN format, let's understand how NCNN models are normally used.
 
 NCNN models, designed for efficiency and performance, are compatible with a variety of deployment platforms:
 
@@ -44,9 +44,9 @@ NCNN models, designed for efficiency and performance, are compatible with a vari
 
 - **Desktop and Server Deployment**: Capable of being deployed in desktop and server environments across Linux, Windows, and macOS, supporting development, training, and evaluation with higher computational capacities.
 
-## Export to NCNN: Converting Your YOLOv8 Model
+## Export to NCNN: Converting Your YOLO11 Model
 
-You can expand model compatibility and deployment flexibility by converting YOLOv8 models to NCNN format.
+You can expand model compatibility and deployment flexibility by converting YOLO11 models to NCNN format.
 
 ### Installation
 
@@ -57,15 +57,15 @@ To install the required packages, run:
     === "CLI"
 
         ```bash
-        # Install the required package for YOLOv8
+        # Install the required package for YOLO11
         pip install ultralytics
         ```
 
-For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
+For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
 
 ### Usage
 
-Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
+Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
 
 !!! example "Usage"
 
@@ -74,14 +74,14 @@ Before diving into the usage instructions, it's important to note that while all
           ```python
           from ultralytics import YOLO
 
-          # Load the YOLOv8 model
-          model = YOLO("yolov8n.pt")
+          # Load the YOLO11 model
+          model = YOLO("yolo11n.pt")
 
           # Export the model to NCNN format
-          model.export(format="ncnn")  # creates '/yolov8n_ncnn_model'
+          model.export(format="ncnn")  # creates '/yolo11n_ncnn_model'
 
           # Load the exported NCNN model
-          ncnn_model = YOLO("./yolov8n_ncnn_model")
+          ncnn_model = YOLO("./yolo11n_ncnn_model")
 
           # Run inference
           results = ncnn_model("https://ultralytics.com/images/bus.jpg")
@@ -90,18 +90,18 @@ Before diving into the usage instructions, it's important to note that while all
     === "CLI"
 
           ```bash
-          # Export a YOLOv8n PyTorch model to NCNN format
-          yolo export model=yolov8n.pt format=ncnn  # creates '/yolov8n_ncnn_model'
+          # Export a YOLO11n PyTorch model to NCNN format
+          yolo export model=yolo11n.pt format=ncnn  # creates '/yolo11n_ncnn_model'
 
           # Run inference with the exported model
-          yolo predict model='./yolov8n_ncnn_model' source='https://ultralytics.com/images/bus.jpg'
+          yolo predict model='./yolo11n_ncnn_model' source='https://ultralytics.com/images/bus.jpg'
           ```
 
 For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
 
-## Deploying Exported YOLOv8 NCNN Models
+## Deploying Exported YOLO11 NCNN Models
 
-After successfully exporting your Ultralytics YOLOv8 models to NCNN format, you can now deploy them. The primary and recommended first step for running a NCNN model is to utilize the YOLO("./model_ncnn_model") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your NCNN models in various other settings, take a look at the following resources:
+After successfully exporting your Ultralytics YOLO11 models to NCNN format, you can now deploy them. The primary and recommended first step for running a NCNN model is to utilize the YOLO("./model_ncnn_model") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your NCNN models in various other settings, take a look at the following resources:
 
 - **[Android](https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-android)**: This blog explains how to use NCNN models for performing tasks like [object detection](https://www.ultralytics.com/glossary/object-detection) through Android applications.
 
@@ -113,40 +113,40 @@ After successfully exporting your Ultralytics YOLOv8 models to NCNN format, you
 
 ## Summary
 
-In this guide, we've gone over exporting Ultralytics YOLOv8 models to the NCNN format. This conversion step is crucial for improving the efficiency and speed of YOLOv8 models, making them more effective and suitable for limited-resource computing environments.
+In this guide, we've gone over exporting Ultralytics YOLO11 models to the NCNN format. This conversion step is crucial for improving the efficiency and speed of YOLO11 models, making them more effective and suitable for limited-resource computing environments.
 
 For detailed instructions on usage, please refer to the [official NCNN documentation](https://ncnn.readthedocs.io/en/latest/index.html).
 
-Also, if you're interested in exploring other integration options for Ultralytics YOLOv8, be sure to visit our [integration guide page](index.md) for further insights and information.
+Also, if you're interested in exploring other integration options for Ultralytics YOLO11, be sure to visit our [integration guide page](index.md) for further insights and information.
 
 ## FAQ
 
-### How do I export Ultralytics YOLOv8 models to NCNN format?
+### How do I export Ultralytics YOLO11 models to NCNN format?
 
-To export your Ultralytics YOLOv8 model to NCNN format, follow these steps:
+To export your Ultralytics YOLO11 model to NCNN format, follow these steps:
 
 - **Python**: Use the `export` function from the YOLO class.
 
     ```python
     from ultralytics import YOLO
 
-    # Load the YOLOv8 model
-    model = YOLO("yolov8n.pt")
+    # Load the YOLO11 model
+    model = YOLO("yolo11n.pt")
 
     # Export to NCNN format
-    model.export(format="ncnn")  # creates '/yolov8n_ncnn_model'
+    model.export(format="ncnn")  # creates '/yolo11n_ncnn_model'
     ```
 
 - **CLI**: Use the `yolo` command with the `export` argument.
     ```bash
-    yolo export model=yolov8n.pt format=ncnn  # creates '/yolov8n_ncnn_model'
+    yolo export model=yolo11n.pt format=ncnn  # creates '/yolo11n_ncnn_model'
     ```
 
 For detailed export options, check the [Export](../modes/export.md) page in the documentation.
 
-### What are the advantages of exporting YOLOv8 models to NCNN?
+### What are the advantages of exporting YOLO11 models to NCNN?
 
-Exporting your Ultralytics YOLOv8 models to NCNN offers several benefits:
+Exporting your Ultralytics YOLO11 models to NCNN offers several benefits:
 
 - **Efficiency**: NCNN models are optimized for mobile and embedded devices, ensuring high performance even with limited computational resources.
 - **Quantization**: NCNN supports techniques like quantization that improve model speed and reduce memory usage.
@@ -174,13 +174,13 @@ NCNN is versatile and supports various platforms:
 
 If running models on a Raspberry Pi isn't fast enough, converting to the NCNN format could speed things up as detailed in our [Raspberry Pi Guide](../guides/raspberry-pi.md).
 
-### How can I deploy Ultralytics YOLOv8 NCNN models on Android?
+### How can I deploy Ultralytics YOLO11 NCNN models on Android?
 
-To deploy your YOLOv8 models on Android:
+To deploy your YOLO11 models on Android:
 
 1. **Build for Android**: Follow the [NCNN Build for Android](https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-android) guide.
 2. **Integrate with Your App**: Use the NCNN Android SDK to integrate the exported model into your application for efficient on-device inference.
 
-For step-by-step instructions, refer to our guide on [Deploying YOLOv8 NCNN Models](#deploying-exported-yolov8-ncnn-models).
+For step-by-step instructions, refer to our guide on [Deploying YOLO11 NCNN Models](#deploying-exported-yolo11-ncnn-models).
 
 For more advanced guides and use cases, visit the [Ultralytics documentation page](../guides/model-deployment-options.md).