- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`
@ -143,28 +143,45 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), which include 1000 pretrained classes.
- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
</details>
<details><summary>Pose (COCO)</summary>
See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples with these models trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), which include 1 pre-trained class, person.
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
@ -177,34 +194,17 @@ See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with
- **mAP<sup>test</sup>** values are for single-model multiscale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
- **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), which include 1000 pretrained classes.
- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
</details>
## <divalign="center">Integrations</div>
Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
@ -103,11 +103,15 @@ Ultralytics YOLO is the latest advancement in the acclaimed YOLO (You Only Look
### How can I get started with YOLO installation and setup?
Getting started with YOLO is quick and straightforward. You can install the Ultralytics package using pip and get up and running in minutes. Here's a basic installation command:
Getting started with YOLO is quick and straightforward. You can install the Ultralytics package using [pip](https://pypi.org/project/ultralytics/) and get up and running in minutes. Here's a basic installation command:
```bash
pip install ultralytics
```
!!! example
=== "CLI"
```bash
pip install ultralytics
```
For a comprehensive step-by-step guide, visit our [quickstart guide](quickstart.md). This resource will help you with installation instructions, initial setup, and running your first model.
@ -119,11 +123,28 @@ Training a custom YOLO model on your dataset involves a few detailed steps:
2. Configure the training parameters in a YAML file.
3. Use the `yolo train` command to start training.
Here's an example command:
Here's example code:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a pre-trained YOLO model (you can choose n, s, m, l, or x versions)
For a detailed walkthrough, check out our [Train a Model](modes/train.md) guide, which includes examples and tips for optimizing your training process.
@ -140,8 +161,27 @@ For more details, visit our [Licensing](https://www.ultralytics.com/license) pag
Ultralytics YOLO supports efficient and customizable multi-object tracking. To utilize tracking capabilities, you can use the `yolo track` command as shown below:
```bash
yolo track model=yolo11n.pt source=video.mp4
```
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a pre-trained YOLO model
model = YOLO("yolo11n.pt")
# Start tracking objects in a video
# You can also use live video streams or webcam input
model.track(source="path/to/video.mp4")
```
=== "CLI"
```bash
# Perform object tracking on a video from the command line
# You can specify different sources like webcam (0) or RTSP streams
yolo track source=path/to/video.mp4
```
For a detailed guide on setting up and running object tracking, check our [tracking mode](modes/track.md) documentation, which explains the configuration and practical applications in real-time scenarios.
description: Discover the diverse modes of Ultralytics YOLOv8, including training, validation, prediction, export, tracking, and benchmarking. Maximize model performance and efficiency.
description: Discover the diverse modes of Ultralytics YOLO11, including training, validation, prediction, export, tracking, and benchmarking. Maximize model performance and efficiency.
<imgwidth="1024"src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif"alt="Ultralytics YOLO ecosystem and integrations">
## Introduction
Ultralytics YOLOv8 is not just another object detection model; it's a versatile framework designed to cover the entire lifecycle of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models—from data ingestion and model training to validation, deployment, and real-world tracking. Each mode serves a specific purpose and is engineered to offer you the flexibility and efficiency required for different tasks and use-cases.
Ultralytics YOLO11 is not just another object detection model; it's a versatile framework designed to cover the entire lifecycle of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models—from data ingestion and model training to validation, deployment, and real-world tracking. Each mode serves a specific purpose and is engineered to offer you the flexibility and efficiency required for different tasks and use-cases.
<palign="center">
<br>
@ -25,7 +25,7 @@ Ultralytics YOLOv8 is not just another object detection model; it's a versatile
### Modes at a Glance
Understanding the different **modes** that Ultralytics YOLOv8 supports is critical to getting the most out of your models:
Understanding the different **modes** that Ultralytics YOLO11 supports is critical to getting the most out of your models:
- **Train** mode: Fine-tune your model on custom or preloaded datasets.
- **Val** mode: A post-training checkpoint to validate model performance.
@ -34,49 +34,49 @@ Understanding the different **modes** that Ultralytics YOLOv8 supports is critic
- **Track** mode: Extend your object detection model into real-time tracking applications.
- **Benchmark** mode: Analyze the speed and accuracy of your model in diverse deployment environments.
This comprehensive guide aims to give you an overview and practical insights into each mode, helping you harness the full potential of YOLOv8.
This comprehensive guide aims to give you an overview and practical insights into each mode, helping you harness the full potential of YOLO11.
## [Train](train.md)
Train mode is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.
Train mode is used for training a YOLO11 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.
[Train Examples](train.md){ .md-button }
## [Val](val.md)
Val mode is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
Val mode is used for validating a YOLO11 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
[Val Examples](val.md){ .md-button }
## [Predict](predict.md)
Predict mode is used for making predictions using a trained YOLOv8 model on new images or videos. In this mode, the model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model predicts the classes and locations of objects in the input images or videos.
Predict mode is used for making predictions using a trained YOLO11 model on new images or videos. In this mode, the model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model predicts the classes and locations of objects in the input images or videos.
[Predict Examples](predict.md){ .md-button }
## [Export](export.md)
Export mode is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the model is converted to a format that can be used by other software applications or hardware devices. This mode is useful when deploying the model to production environments.
Export mode is used for exporting a YOLO11 model to a format that can be used for deployment. In this mode, the model is converted to a format that can be used by other software applications or hardware devices. This mode is useful when deploying the model to production environments.
[Export Examples](export.md){ .md-button }
## [Track](track.md)
Track mode is used for tracking objects in real-time using a YOLOv8 model. In this mode, the model is loaded from a checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful for applications such as surveillance systems or self-driving cars.
Track mode is used for tracking objects in real-time using a YOLO11 model. In this mode, the model is loaded from a checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful for applications such as surveillance systems or self-driving cars.
[Track Examples](track.md){ .md-button }
## [Benchmark](benchmark.md)
Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation, and pose) or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various formats like ONNX, OpenVINO, TensorRT, and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.
Benchmark mode is used to profile the speed and accuracy of various export formats for YOLO11. The benchmarks provide information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation, and pose) or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various formats like ONNX, OpenVINO, TensorRT, and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.
[Benchmark Examples](benchmark.md){ .md-button }
## FAQ
### How do I train a custom [object detection](https://www.ultralytics.com/glossary/object-detection) model with Ultralytics YOLOv8?
### How do I train a custom [object detection](https://www.ultralytics.com/glossary/object-detection) model with Ultralytics YOLO11?
Training a custom object detection model with Ultralytics YOLOv8 involves using the train mode. You need a dataset formatted in YOLO format, containing images and corresponding annotation files. Use the following command to start the training process:
Training a custom object detection model with Ultralytics YOLO11 involves using the train mode. You need a dataset formatted in YOLO format, containing images and corresponding annotation files. Use the following command to start the training process:
!!! example
@ -85,22 +85,25 @@ Training a custom object detection model with Ultralytics YOLOv8 involves using
```python
from ultralytics import YOLO
# Train a custom model
model = YOLO("yolov8n.pt")
# Load a pre-trained YOLO model (you can choose n, s, m, l, or x versions)
For more detailed instructions, you can refer to the [Ultralytics Train Guide](../modes/train.md).
### What metrics does Ultralytics YOLOv8 use to validate the model's performance?
### What metrics does Ultralytics YOLO11 use to validate the model's performance?
Ultralytics YOLOv8 uses various metrics during the validation process to assess model performance. These include:
Ultralytics YOLO11 uses various metrics during the validation process to assess model performance. These include:
- **mAP (mean Average Precision)**: This evaluates the accuracy of object detection.
- **IOU (Intersection over Union)**: Measures the overlap between predicted and ground truth bounding boxes.
@ -115,22 +118,25 @@ You can run the following command to start the validation:
```python
from ultralytics import YOLO
# Validate the model
model = YOLO("yolov8n.pt")
# Load a pre-trained or custom YOLO model
model = YOLO("yolo11n.pt")
# Run validation on your dataset
model.val(data="path/to/validation.yaml")
```
=== "CLI"
```bash
# Validate a YOLO model from the command line
yolo val data=path/to/validation.yaml
```
Refer to the [Validation Guide](../modes/val.md) for further details.
### How can I export my YOLOv8 model for deployment?
### How can I export my YOLO11 model for deployment?
Ultralytics YOLOv8 offers export functionality to convert your trained model into various deployment formats such as ONNX, TensorRT, CoreML, and more. Use the following example to export your model:
Ultralytics YOLO11 offers export functionality to convert your trained model into various deployment formats such as ONNX, TensorRT, CoreML, and more. Use the following example to export your model:
!!! example
@ -139,22 +145,25 @@ Ultralytics YOLOv8 offers export functionality to convert your trained model int
```python
from ultralytics import YOLO
# Export the model
model = YOLO("yolov8n.pt")
# Load your trained YOLO model
model = YOLO("yolo11n.pt")
# Export the model to ONNX format (you can specify other formats as needed)
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolov8n.pt format=onnx
# Export a YOLO model to ONNX format from the command line
yolo export model=yolo11n.pt format=onnx
```
Detailed steps for each export format can be found in the [Export Guide](../modes/export.md).
### What is the purpose of the benchmark mode in Ultralytics YOLOv8?
### What is the purpose of the benchmark mode in Ultralytics YOLO11?
Benchmark mode in Ultralytics YOLOv8 is used to analyze the speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) of various export formats such as ONNX, TensorRT, and OpenVINO. It provides metrics like model size, `mAP50-95` for object detection, and inference time across different hardware setups, helping you choose the most suitable format for your deployment needs.
Benchmark mode in Ultralytics YOLO11 is used to analyze the speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) of various export formats such as ONNX, TensorRT, and OpenVINO. It provides metrics like model size, `mAP50-95` for object detection, and inference time across different hardware setups, helping you choose the most suitable format for your deployment needs.
!!! example
@ -163,21 +172,24 @@ Benchmark mode in Ultralytics YOLOv8 is used to analyze the speed and [accuracy]
```python
from ultralytics.utils.benchmarks import benchmark
For more details, refer to the [Benchmark Guide](../modes/benchmark.md).
### How can I perform real-time object tracking using Ultralytics YOLOv8?
### How can I perform real-time object tracking using Ultralytics YOLO11?
Real-time object tracking can be achieved using the track mode in Ultralytics YOLOv8. This mode extends object detection capabilities to track objects across video frames or live feeds. Use the following example to enable tracking:
Real-time object tracking can be achieved using the track mode in Ultralytics YOLO11. This mode extends object detection capabilities to track objects across video frames or live feeds. Use the following example to enable tracking:
!!! example
@ -186,14 +198,19 @@ Real-time object tracking can be achieved using the track mode in Ultralytics YO
```python
from ultralytics import YOLO
# Track objects in a video
model = YOLO("yolov8n.pt")
# Load a pre-trained YOLO model
model = YOLO("yolo11n.pt")
# Start tracking objects in a video
# You can also use live video streams or webcam input
model.track(source="path/to/video.mp4")
```
=== "CLI"
```bash
# Perform object tracking on a video from the command line
# You can specify different sources like webcam (0) or RTSP streams
@ -34,13 +34,13 @@ YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose model
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
description: Learn about object detection with YOLOv8. Explore pretrained models, training, validation, prediction, and export details for efficient object recognition.
description: Learn about object detection with YOLO11. Explore pretrained models, training, validation, prediction, and export details for efficient object recognition.
@ -33,16 +33,16 @@ YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco8.yaml batch=1 device=0|cpu`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`
description: Explore Ultralytics YOLOv8 for detection, segmentation, classification, OBB, and pose estimation with high accuracy and speed. Learn how to apply each task.
description: Explore Ultralytics YOLO11 for detection, segmentation, classification, OBB, and pose estimation with high accuracy and speed. Learn how to apply each task.
YOLOv8 is an AI framework that supports multiple [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) **tasks**. The framework can be used to perform [detection](detect.md), [segmentation](segment.md), [obb](obb.md), [classification](classify.md), and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
YOLO11 is an AI framework that supports multiple [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) **tasks**. The framework can be used to perform [detection](detect.md), [segmentation](segment.md), [obb](obb.md), [classification](classify.md), and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
<palign="center">
<br>
@ -24,43 +24,43 @@ YOLOv8 is an AI framework that supports multiple [computer vision](https://www.u
## [Detection](detect.md)
Detection is the primary task supported by YOLOv8. It involves detecting objects in an image or video frame and drawing bounding boxes around them. The detected objects are classified into different categories based on their features. YOLOv8 can detect multiple objects in a single image or video frame with high [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed.
Detection is the primary task supported by YOLO11. It involves detecting objects in an image or video frame and drawing bounding boxes around them. The detected objects are classified into different categories based on their features. YOLO11 can detect multiple objects in a single image or video frame with high [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed.
[Detection Examples](detect.md){ .md-button }
## [Segmentation](segment.md)
Segmentation is a task that involves segmenting an image into different regions based on the content of the image. Each region is assigned a label based on its content. This task is useful in applications such as [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) and medical imaging. YOLOv8 uses a variant of the U-Net architecture to perform segmentation.
Segmentation is a task that involves segmenting an image into different regions based on the content of the image. Each region is assigned a label based on its content. This task is useful in applications such as [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) and medical imaging. YOLO11 uses a variant of the U-Net architecture to perform segmentation.
[Segmentation Examples](segment.md){ .md-button }
## [Classification](classify.md)
Classification is a task that involves classifying an image into different categories. YOLOv8 can be used to classify images based on their content. It uses a variant of the EfficientNet architecture to perform classification.
Classification is a task that involves classifying an image into different categories. YOLO11 can be used to classify images based on their content. It uses a variant of the EfficientNet architecture to perform classification.
Pose/keypoint detection is a task that involves detecting specific points in an image or video frame. These points are referred to as keypoints and are used to track movement or pose estimation. YOLOv8 can detect keypoints in an image or video frame with high accuracy and speed.
Pose/keypoint detection is a task that involves detecting specific points in an image or video frame. These points are referred to as keypoints and are used to track movement or pose estimation. YOLO11 can detect keypoints in an image or video frame with high accuracy and speed.
[Pose Examples](pose.md){ .md-button }
## [OBB](obb.md)
Oriented object detection goes a step further than regular object detection with introducing an extra angle to locate objects more accurate in an image. YOLOv8 can detect rotated objects in an image or video frame with high accuracy and speed.
Oriented object detection goes a step further than regular object detection with introducing an extra angle to locate objects more accurate in an image. YOLO11 can detect rotated objects in an image or video frame with high accuracy and speed.
[Oriented Detection](obb.md){ .md-button }
## Conclusion
YOLOv8 supports multiple tasks, including detection, segmentation, classification, oriented object detection and keypoints detection. Each of these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose the appropriate task for your computer vision application.
YOLO11 supports multiple tasks, including detection, segmentation, classification, oriented object detection and keypoints detection. Each of these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose the appropriate task for your computer vision application.
## FAQ
### What tasks can Ultralytics YOLOv8 perform?
### What tasks can Ultralytics YOLO11 perform?
Ultralytics YOLOv8 is a versatile AI framework capable of performing various computer vision tasks with high accuracy and speed. These tasks include:
Ultralytics YOLO11 is a versatile AI framework capable of performing various computer vision tasks with high accuracy and speed. These tasks include:
- **[Detection](detect.md):** Identifying and localizing objects in images or video frames by drawing bounding boxes around them.
- **[Segmentation](segment.md):** Segmenting images into different regions based on their content, useful for applications like medical imaging.
@ -68,12 +68,12 @@ Ultralytics YOLOv8 is a versatile AI framework capable of performing various com
- **[Pose estimation](pose.md):** Detecting specific keypoints in an image or video frame to track movements or poses.
- **[Oriented Object Detection (OBB)](obb.md):** Detecting rotated objects with an added orientation angle for enhanced accuracy.
### How do I use Ultralytics YOLOv8 for object detection?
### How do I use Ultralytics YOLO11 for object detection?
To use Ultralytics YOLOv8 for object detection, follow these steps:
To use Ultralytics YOLO11 for object detection, follow these steps:
1. Prepare your dataset in the appropriate format.
2. Train the YOLOv8 model using the detection task.
2. Train the YOLO11 model using the detection task.
3. Use the model to make predictions by feeding in new images or video frames.
!!! example
@ -83,38 +83,44 @@ To use Ultralytics YOLOv8 for object detection, follow these steps:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt") # Load pre-trained model
# Load a pre-trained YOLO model (adjust model type as needed)
model = YOLO("yolo11n.pt") # n, s, m, l, x versions available
# Perform object detection on an image
results = model.predict(source="image.jpg") # Can also use video, directory, URL, etc.
# Display the results
results[0].show() # Show the first image results
```
=== "CLI"
```bash
yolo detect model=yolov8n.pt source='image.jpg'
# Run YOLO detection from the command line
yolo detect model=yolo11n.pt source="image.jpg" # Adjust model and source as needed
```
For more detailed instructions, check out our [detection examples](detect.md).
### What are the benefits of using YOLOv8 for segmentation tasks?
### What are the benefits of using YOLO11 for segmentation tasks?
Using YOLOv8 for segmentation tasks provides several advantages:
Using YOLO11 for segmentation tasks provides several advantages:
1. **High Accuracy:** The segmentation task leverages a variant of the U-Net architecture to achieve precise segmentation.
2. **Speed:** YOLOv8 is optimized for real-time applications, offering quick processing even for high-resolution images.
2. **Speed:** YOLO11 is optimized for real-time applications, offering quick processing even for high-resolution images.
3. **Multiple Applications:** It is ideal for medical imaging, autonomous driving, and other applications requiring detailed image segmentation.
Learn more about the benefits and use cases of YOLOv8 for segmentation in the [segmentation section](segment.md).
Learn more about the benefits and use cases of YOLO11 for segmentation in the [segmentation section](segment.md).
### Can Ultralytics YOLOv8 handle pose estimation and keypoint detection?
### Can Ultralytics YOLO11 handle pose estimation and keypoint detection?
Yes, Ultralytics YOLOv8 can effectively perform pose estimation and keypoint detection with high accuracy and speed. This feature is particularly useful for tracking movements in sports analytics, healthcare, and human-computer interaction applications. YOLOv8 detects keypoints in an image or video frame, allowing for precise pose estimation.
Yes, Ultralytics YOLO11 can effectively perform pose estimation and keypoint detection with high accuracy and speed. This feature is particularly useful for tracking movements in sports analytics, healthcare, and human-computer interaction applications. YOLO11 detects keypoints in an image or video frame, allowing for precise pose estimation.
For more details and implementation tips, visit our [pose estimation examples](pose.md).
### Why should I choose Ultralytics YOLOv8 for oriented object detection (OBB)?
### Why should I choose Ultralytics YOLO11 for oriented object detection (OBB)?
Oriented Object Detection (OBB) with YOLOv8 provides enhanced [precision](https://www.ultralytics.com/glossary/precision) by detecting objects with an additional angle parameter. This feature is beneficial for applications requiring accurate localization of rotated objects, such as aerial imagery analysis and warehouse automation.
Oriented Object Detection (OBB) with YOLO11 provides enhanced [precision](https://www.ultralytics.com/glossary/precision) by detecting objects with an additional angle parameter. This feature is beneficial for applications requiring accurate localization of rotated objects, such as aerial imagery analysis and warehouse automation.
- **Increased Precision:** The angle component reduces false positives for rotated objects.
- **Versatile Applications:** Useful for tasks in geospatial analysis, robotics, etc.
description: Discover how to detect objects with rotation for higher precision using YOLOv8 OBB models. Learn, train, validate, and export OBB models effortlessly.
keywords: Oriented Bounding Boxes, OBB, Object Detection, YOLOv8, Ultralytics, DOTAv1, Model Training, Model Export, AI, Machine Learning
model_name: yolov8n-obb
description: Discover how to detect objects with rotation for higher precision using YOLO11 OBB models. Learn, train, validate, and export OBB models effortlessly.
keywords: Oriented Bounding Boxes, OBB, Object Detection, YOLO11, Ultralytics, DOTAv1, Model Training, Model Export, AI, Machine Learning
@ -42,15 +42,15 @@ YOLOv8 pretrained OBB models are shown here, which are pretrained on the [DOTAv1
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
- **mAP<sup>test</sup>** values are for single-model multiscale on [DOTAv1 test](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
- **mAP<sup>test</sup>** values are for single-model multiscale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
- **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
description: Discover how to use YOLOv8 for pose estimation tasks. Learn about model training, validation, prediction, and exporting in various formats.
keywords: pose estimation, YOLOv8, Ultralytics, keypoints, model training, image recognition, deep learning
model_name: yolov8n-pose
description: Discover how to use YOLO11 for pose estimation tasks. Learn about model training, validation, prediction, and exporting in various formats.
keywords: pose estimation, YOLO11, Ultralytics, keypoints, model training, image recognition, deep learning
model_name: yolo11n-pose
---
# Pose Estimation
@ -66,17 +66,16 @@ YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models ar
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
@ -34,16 +34,16 @@ YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco8-seg.yaml batch=1 device=0|cpu`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
"tensorstore>=0.1.63; platform_machine == 'aarch64' and python_version >= '3.9'",# for TF Raspberry Pi exports
"keras",# not installed automatically by tensorflow>=2.16
"tensorstore>=0.1.63; platform_machine == 'aarch64' and python_version >= '3.9'",# for TF Raspberry Pi exports
"keras",# not installed automatically by tensorflow>=2.16
"flatbuffers>=23.5.26,<100; platform_machine == 'aarch64'",# update old 'flatbuffers' included inside tensorflow package
"numpy==1.23.5; platform_machine == 'aarch64'",# fix error: `np.bool` was a deprecated alias for the builtin `bool` when using TensorRT models on NVIDIA Jetson
"h5py!=3.11.0; platform_machine == 'aarch64'",# fix h5py build issues due to missing aarch64 wheels in 3.11 release
Glenn Jocher
5 months ago
committed by
GitHub