Update YOLO11 Actions and Docs (#16596)
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@ -21,13 +21,13 @@ The [COCO](https://cocodataset.org/#home) (Common Objects in Context) dataset is
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## COCO Pretrained Models
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| 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) |
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| 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) |
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| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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## Key Features
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@ -60,7 +60,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
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## Usage
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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.
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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.
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!!! example "Train Example"
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@ -70,7 +70,7 @@ To train a YOLOv8n model on the COCO dataset for 100 [epochs](https://www.ultral
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="coco.yaml", epochs=100, imgsz=640)
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@ -80,7 +80,7 @@ To train a YOLOv8n model on the COCO dataset for 100 [epochs](https://www.ultral
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```bash
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# Start training from a pretrained *.pt model
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yolo detect train data=coco.yaml model=yolov8n.pt epochs=100 imgsz=640
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yolo detect train data=coco.yaml model=yolo11n.pt epochs=100 imgsz=640
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```
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## Sample Images and Annotations
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@ -122,7 +122,7 @@ The [COCO dataset](https://cocodataset.org/#home) (Common Objects in Context) is
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### How can I train a YOLO model using the COCO dataset?
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To train a YOLOv8 model using the COCO dataset, you can use the following code snippets:
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To train a YOLO11 model using the COCO dataset, you can use the following code snippets:
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!!! example "Train Example"
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@ -132,7 +132,7 @@ To train a YOLOv8 model using the COCO dataset, you can use the following code s
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="coco.yaml", epochs=100, imgsz=640)
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@ -142,7 +142,7 @@ To train a YOLOv8 model using the COCO dataset, you can use the following code s
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```bash
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# Start training from a pretrained *.pt model
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yolo detect train data=coco.yaml model=yolov8n.pt epochs=100 imgsz=640
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yolo detect train data=coco.yaml model=yolo11n.pt epochs=100 imgsz=640
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```
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Refer to the [Training page](../../modes/train.md) for more details on available arguments.
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@ -156,13 +156,13 @@ The COCO dataset includes:
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- Standardized evaluation metrics for object detection (mAP) and segmentation (mean Average Recall, mAR).
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- **Mosaicing** technique in training batches to enhance model generalization across various object sizes and contexts.
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### Where can I find pretrained YOLOv8 models trained on the COCO dataset?
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### Where can I find pretrained YOLO11 models trained on the COCO dataset?
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Pretrained YOLOv8 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
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Pretrained YOLO11 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
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- [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt)
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- [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt)
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- [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt)
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- [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt)
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- [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt)
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- [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt)
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These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.
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