@ -124,13 +124,13 @@ All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cf
See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [COCO](https://docs.ultralytics.com/datasets/detect/coco/), which include 80 pre-trained classes.
- **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`
@ -141,13 +141,13 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples with these models trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), which include 80 pre-trained classes.
- **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`
@ -158,13 +158,13 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
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`
@ -175,13 +175,13 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples wit
See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
- **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`
@ -192,13 +192,13 @@ See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with
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`
YOLO11 is the latest iteration in the Ultralytics YOLO series of real-time object detectors, redefining what's possible with cutting-edge accuracy, speed, and efficiency. Building upon the impressive advancements of previous YOLO versions, YOLO11 introduces significant improvements in architecture and training methods, making it a versatile choice for a wide range of computer vision tasks.
YOLO11 is the latest iteration in the [Ultralytics](https://www.ultralytics.com) YOLO series of real-time object detectors, redefining what's possible with cutting-edge [accuracy](https://www.ultralytics.com/glossary/accuracy), speed, and efficiency. Building upon the impressive advancements of previous YOLO versions, YOLO11 introduces significant improvements in architecture and training methods, making it a versatile choice for a wide range of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
<strong>Watch:</strong> Ultralytics YOLO11 Announcement at #YV24
<strong>Watch:</strong> Ultralytics YOLO11 Announcement at [YOLO Vision 2024](https://www.ultralytics.com/events/yolovision)
</p>
## Key Features
- **Enhanced Feature Extraction:** YOLO11 employs an improved backbone and neck architecture, which enhances [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) capabilities for more precise object detection and complex task performance.
- **Optimized for Efficiency and Speed:** YOLO11 introduces refined architectural designs and optimized training pipelines, delivering faster processing speeds and maintaining an optimal balance between accuracy and performance.
- **Greater Accuracy with Fewer Parameters:** With advancements in model design, YOLO11m achieves a higher mean Average Precision (mAP) on the COCO dataset while using 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
- **Greater Accuracy with Fewer Parameters:** With advancements in model design, YOLO11m achieves a higher [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) on the COCO dataset while using 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
- **Adaptability Across Environments:** YOLO11 can be seamlessly deployed across various environments, including edge devices, cloud platforms, and systems supporting NVIDIA GPUs, ensuring maximum flexibility.
- **Broad Range of Supported Tasks:** Whether it's object detection, instance segmentation, image classification, pose estimation, or oriented object detection (OBB), YOLO11 is designed to cater to a diverse set of computer vision challenges.
@ -53,7 +53,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
See [Detection Docs](../tasks/detect.md) for usage examples with these models trained on [COCO](../datasets/detect/coco.md), which include 80 pre-trained classes.
@ -65,7 +65,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
See [Segmentation Docs](../tasks/segment.md) for usage examples with these models trained on [COCO](../datasets/segment/coco.md), which include 80 pre-trained classes.
@ -77,7 +77,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
See [Classification Docs](../tasks/classify.md) for usage examples with these models trained on [ImageNet](../datasets/classify/imagenet.md), which include 1000 pre-trained classes.
@ -89,7 +89,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
See [Pose Estimation Docs](../tasks/pose.md) for usage examples with these models trained on [COCO](../datasets/pose/coco.md), which include 1 pre-trained class, 'person'.
@ -101,7 +101,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
See [Oriented Detection Docs](../tasks/obb.md) for usage examples with these models trained on [DOTAv1](../datasets/obb/dota-v2.md#dota-v10), which include 15 pre-trained classes.
@ -113,7 +113,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
This section provides simple YOLO11 training and inference examples. For full documentation on these and other [modes](../modes/index.md), see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md), and [Export](../modes/export.md) docs pages.
Note that the example below is for YOLO11 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks, see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md), [OBB](../tasks/obb.md), and [Pose](../tasks/pose.md) docs.
Note that the example below is for YOLO11 [Detect](../tasks/detect.md) models for [object detection](https://www.ultralytics.com/glossary/object-detection). For additional supported tasks, see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md), [OBB](../tasks/obb.md), and [Pose](../tasks/pose.md) docs.
!!! example
@ -176,9 +176,9 @@ Ultralytics YOLO11 introduces several significant advancements over its predeces
- **Enhanced Feature Extraction:** YOLO11 employs an improved backbone and neck architecture, enhancing [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) capabilities for more precise object detection.
- **Optimized Efficiency and Speed:** Refined architectural designs and optimized training pipelines deliver faster processing speeds while maintaining a balance between accuracy and performance.
- **Greater Accuracy with Fewer Parameters:** YOLO11m achieves higher mean Average Precision (mAP) on the COCO dataset with 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
- **Greater Accuracy with Fewer Parameters:** YOLO11m achieves higher mean Average [Precision](https://www.ultralytics.com/glossary/precision) (mAP) on the COCO dataset with 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
- **Adaptability Across Environments:** YOLO11 can be deployed across various environments, including edge devices, cloud platforms, and systems supporting NVIDIA GPUs.
- **Broad Range of Supported Tasks:** YOLO11 supports diverse computer vision tasks such as object detection, instance segmentation, image classification, pose estimation, and oriented object detection (OBB).
- **Broad Range of Supported Tasks:** YOLO11 supports diverse computer vision tasks such as object detection, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), image classification, pose estimation, and oriented object detection (OBB).
### How do I train a YOLO11 model for object detection?
@ -213,7 +213,7 @@ YOLO11 models are versatile and support a wide range of computer vision tasks, i
- **Object Detection:** Identifying and locating objects within an image.
- **Instance Segmentation:** Detecting objects and delineating their boundaries.
- **Image Classification:** Categorizing images into predefined classes.
- **[Image Classification](https://www.ultralytics.com/glossary/image-classification):** Categorizing images into predefined classes.
- **Pose Estimation:** Detecting and tracking keypoints on human bodies.
- **Oriented Object Detection (OBB):** Detecting objects with rotation for higher precision.
YOLOv6 provides various pre-trained models with different scales:
- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA Tesla T4 GPU.
- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA T4 GPU.
- YOLOv6-S: 45.0% AP at 484 FPS.
- YOLOv6-M: 50.0% AP at 226 FPS.
- YOLOv6-L: 52.8% AP at 116 FPS.
@ -151,7 +151,7 @@ YOLOv6 offers multiple versions, each optimized for different performance requir
- YOLOv6-L: 52.8% AP at 116 FPS
- YOLOv6-L6: State-of-the-art accuracy in real-time scenarios
These models are evaluated on the COCO dataset using an NVIDIA Tesla T4 GPU. For more on performance metrics, see the [Performance Metrics](#performance-metrics) section.
These models are evaluated on the COCO dataset using an NVIDIA T4 GPU. For more on performance metrics, see the [Performance Metrics](#performance-metrics) section.
### How does the Anchor-Aided Training (AAT) strategy benefit YOLOv6?
@ -18,7 +18,7 @@ Let's begin by creating a virtual machine that's tuned for deep learning:
1. Head over to the [GCP marketplace](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning) and select the **Deep Learning VM**.
2. Opt for a **n1-standard-8** instance; it offers a balance of 8 vCPUs and 30 GB of memory, ideally suited for our needs.
3. Next, select a GPU. This depends on your workload; even a basic one like the Tesla T4 will markedly accelerate your model training.
3. Next, select a GPU. This depends on your workload; even a basic one like the T4 will markedly accelerate your model training.
4. Tick the box for 'Install NVIDIA GPU driver automatically on first startup?' for hassle-free setup.
5. Allocate a 300 GB SSD Persistent Disk to ensure you don't bottleneck on I/O operations.
6. Hit 'Deploy' and let GCP do its magic in provisioning your custom Deep Learning VM.
Glenn Jocher
2 months ago
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