YOLO11 Tasks, Modes, Usage, Macros and Solutions Updates (#16593)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
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@ -17,7 +17,7 @@ The output of an oriented object detector is a set of rotated bounding boxes tha
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!!! tip
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YOLOv8 OBB models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
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YOLO11 OBB models use the `-obb` suffix, i.e. `yolo11n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
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<p align="center">
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<br>
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@ -36,14 +36,14 @@ The output of an oriented object detector is a set of rotated bounding boxes tha
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| :------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------: |
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## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
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## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11)
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YOLOv8 pretrained OBB models are shown here, which are pretrained on the [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml) dataset.
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YOLO11 pretrained OBB models are shown here, which are pretrained on the [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml) dataset.
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[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.
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| Model | size<br><sup>(pixels) | mAP<sup>test<br>50 | 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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| -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.6 ± 0.8 | 4.4 ± 0.0 | 2.7 | 17.2 |
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| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.4 ± 4.0 | 5.1 ± 0.0 | 9.7 | 57.5 |
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| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.8 ± 2.9 | 10.1 ± 0.4 | 20.9 | 183.5 |
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@ -55,7 +55,7 @@ YOLOv8 pretrained OBB models are shown here, which are pretrained on the [DOTAv1
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## Train
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Train YOLOv8n-obb on the `dota8.yaml` dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
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Train YOLO11n-obb on the `dota8.yaml` dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
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!!! example
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@ -65,9 +65,9 @@ Train YOLOv8n-obb on the `dota8.yaml` dataset for 100 [epochs](https://www.ultra
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-obb.yaml") # build a new model from YAML
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model = YOLO("yolov8n-obb.pt") # load a pretrained model (recommended for training)
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model = YOLO("yolov8n-obb.yaml").load("yolov8n.pt") # build from YAML and transfer weights
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model = YOLO("yolo11n-obb.yaml") # build a new model from YAML
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model = YOLO("yolo11n-obb.pt") # load a pretrained model (recommended for training)
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model = YOLO("yolo11n-obb.yaml").load("yolo11n.pt") # build from YAML and transfer weights
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# Train the model
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results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
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@ -77,13 +77,13 @@ Train YOLOv8n-obb on the `dota8.yaml` dataset for 100 [epochs](https://www.ultra
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```bash
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# Build a new model from YAML and start training from scratch
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yolo obb train data=dota8.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
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yolo obb train data=dota8.yaml model=yolo11n-obb.yaml epochs=100 imgsz=640
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# Start training from a pretrained *.pt model
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yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
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yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
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# Build a new model from YAML, transfer pretrained weights to it and start training
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yolo obb train data=dota8.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640
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yolo obb train data=dota8.yaml model=yolo11n-obb.yaml pretrained=yolo11n-obb.pt epochs=100 imgsz=640
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```
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<p align="center">
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@ -103,7 +103,7 @@ OBB dataset format can be found in detail in the [Dataset Guide](../datasets/obb
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## Val
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Validate trained YOLOv8n-obb model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the DOTA8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
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Validate trained YOLO11n-obb model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the DOTA8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
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!!! example
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@ -113,7 +113,7 @@ Validate trained YOLOv8n-obb model [accuracy](https://www.ultralytics.com/glossa
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-obb.pt") # load an official model
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model = YOLO("yolo11n-obb.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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# Validate the model
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@ -127,13 +127,13 @@ Validate trained YOLOv8n-obb model [accuracy](https://www.ultralytics.com/glossa
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=== "CLI"
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```bash
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yolo obb val model=yolov8n-obb.pt data=dota8.yaml # val official model
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yolo obb val model=yolo11n-obb.pt data=dota8.yaml # val official model
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yolo obb val model=path/to/best.pt data=path/to/data.yaml # val custom model
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```
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## Predict
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Use a trained YOLOv8n-obb model to run predictions on images.
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Use a trained YOLO11n-obb model to run predictions on images.
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!!! example
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@ -143,7 +143,7 @@ Use a trained YOLOv8n-obb model to run predictions on images.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-obb.pt") # load an official model
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model = YOLO("yolo11n-obb.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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# Predict with the model
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@ -153,7 +153,7 @@ Use a trained YOLOv8n-obb model to run predictions on images.
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=== "CLI"
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```bash
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yolo obb predict model=yolov8n-obb.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo obb predict model=yolo11n-obb.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo obb predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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@ -172,7 +172,7 @@ See full `predict` mode details in the [Predict](../modes/predict.md) page.
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## Export
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Export a YOLOv8n-obb model to a different format like ONNX, CoreML, etc.
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Export a YOLO11n-obb model to a different format like ONNX, CoreML, etc.
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!!! example
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@ -182,7 +182,7 @@ Export a YOLOv8n-obb model to a different format like ONNX, CoreML, etc.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-obb.pt") # load an official model
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model = YOLO("yolo11n-obb.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained model
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# Export the model
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@ -192,11 +192,11 @@ Export a YOLOv8n-obb model to a different format like ONNX, CoreML, etc.
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=== "CLI"
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```bash
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yolo export model=yolov8n-obb.pt format=onnx # export official model
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yolo export model=yolo11n-obb.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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Available YOLOv8-obb export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-obb.onnx`. Usage examples are shown for your model after export completes.
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Available YOLO11-obb export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolo11n-obb.onnx`. Usage examples are shown for your model after export completes.
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{% include "macros/export-table.md" %}
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@ -208,9 +208,9 @@ See full `export` details in the [Export](../modes/export.md) page.
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Oriented Bounding Boxes (OBB) include an additional angle to enhance object localization accuracy in images. Unlike regular bounding boxes, which are axis-aligned rectangles, OBBs can rotate to fit the orientation of the object better. This is particularly useful for applications requiring precise object placement, such as aerial or satellite imagery ([Dataset Guide](../datasets/obb/index.md)).
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### How do I train a YOLOv8n-obb model using a custom dataset?
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### How do I train a YOLO11n-obb model using a custom dataset?
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To train a YOLOv8n-obb model with a custom dataset, follow the example below using Python or CLI:
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To train a YOLO11n-obb model with a custom dataset, follow the example below using Python or CLI:
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!!! example
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@ -220,7 +220,7 @@ To train a YOLOv8n-obb model with a custom dataset, follow the example below usi
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from ultralytics import YOLO
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# Load a pretrained model
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model = YOLO("yolov8n-obb.pt")
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model = YOLO("yolo11n-obb.pt")
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# Train the model
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results = model.train(data="path/to/custom_dataset.yaml", epochs=100, imgsz=640)
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=== "CLI"
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```bash
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yolo obb train data=path/to/custom_dataset.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
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yolo obb train data=path/to/custom_dataset.yaml model=yolo11n-obb.pt epochs=100 imgsz=640
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```
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For more training arguments, check the [Configuration](../usage/cfg.md) section.
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### What datasets can I use for training YOLOv8-OBB models?
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### What datasets can I use for training YOLO11-OBB models?
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YOLOv8-OBB models are pretrained on datasets like [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml) but you can use any dataset formatted for OBB. Detailed information on OBB dataset formats can be found in the [Dataset Guide](../datasets/obb/index.md).
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YOLO11-OBB models are pretrained on datasets like [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml) but you can use any dataset formatted for OBB. Detailed information on OBB dataset formats can be found in the [Dataset Guide](../datasets/obb/index.md).
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### How can I export a YOLOv8-OBB model to ONNX format?
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### How can I export a YOLO11-OBB model to ONNX format?
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Exporting a YOLOv8-OBB model to ONNX format is straightforward using either Python or CLI:
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Exporting a YOLO11-OBB model to ONNX format is straightforward using either Python or CLI:
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!!! example
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@ -250,7 +250,7 @@ Exporting a YOLOv8-OBB model to ONNX format is straightforward using either Pyth
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-obb.pt")
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model = YOLO("yolo11n-obb.pt")
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# Export the model
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model.export(format="onnx")
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@ -259,14 +259,14 @@ Exporting a YOLOv8-OBB model to ONNX format is straightforward using either Pyth
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=== "CLI"
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```bash
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yolo export model=yolov8n-obb.pt format=onnx
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yolo export model=yolo11n-obb.pt format=onnx
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```
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For more export formats and details, refer to the [Export](../modes/export.md) page.
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### How do I validate the accuracy of a YOLOv8n-obb model?
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### How do I validate the accuracy of a YOLO11n-obb model?
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To validate a YOLOv8n-obb model, you can use Python or CLI commands as shown below:
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To validate a YOLO11n-obb model, you can use Python or CLI commands as shown below:
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!!! example
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@ -276,7 +276,7 @@ To validate a YOLOv8n-obb model, you can use Python or CLI commands as shown bel
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-obb.pt")
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model = YOLO("yolo11n-obb.pt")
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# Validate the model
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metrics = model.val(data="dota8.yaml")
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@ -285,7 +285,7 @@ To validate a YOLOv8n-obb model, you can use Python or CLI commands as shown bel
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=== "CLI"
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```bash
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yolo obb val model=yolov8n-obb.pt data=dota8.yaml
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yolo obb val model=yolo11n-obb.pt data=dota8.yaml
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```
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See full validation details in the [Val](../modes/val.md) section.
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