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285 lines
11 KiB
285 lines
11 KiB
--- |
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comments: true |
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description: Discover how to detect objects with rotation for higher precision using YOLO11 OBB models. Learn, train, validate, and export OBB models effortlessly. |
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keywords: Oriented Bounding Boxes, OBB, Object Detection, YOLO11, Ultralytics, DOTAv1, Model Training, Model Export, AI, Machine Learning |
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model_name: yolo11n-obb |
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--- |
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# Oriented Bounding Boxes [Object Detection](https://www.ultralytics.com/glossary/object-detection) |
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<!-- obb task poster --> |
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Oriented object detection goes a step further than object detection and introduce an extra angle to locate objects more accurate in an image. |
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The output of an oriented object detector is a set of rotated bounding boxes that exactly enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape. |
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<!-- youtube video link for obb task --> |
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!!! tip |
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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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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Z7Z9pHF8wJc" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> Object Detection using Ultralytics YOLO Oriented Bounding Boxes (YOLO-OBB) |
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</p> |
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## Visual Samples |
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| Ships Detection using OBB | Vehicle Detection using OBB | |
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| :------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------: | |
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## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11) |
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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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{% include "macros/yolo-obb-perf.md" %} |
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- **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). |
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- **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` |
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## Train |
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Train YOLO11n-obb on the DOTA8 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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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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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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``` |
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=== "CLI" |
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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=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=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=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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<br> |
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/uZ7SymQfqKI" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> How to Train Ultralytics YOLO-OBB (Oriented Bounding Boxes) Models on DOTA Dataset using Ultralytics HUB |
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</p> |
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### Dataset format |
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OBB dataset format can be found in detail in the [Dataset Guide](../datasets/obb/index.md). |
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## Val |
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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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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a 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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metrics = model.val(data="dota8.yaml") # no arguments needed, dataset and settings remembered |
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metrics.box.map # map50-95(B) |
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metrics.box.map50 # map50(B) |
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metrics.box.map75 # map75(B) |
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metrics.box.maps # a list contains map50-95(B) of each category |
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``` |
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=== "CLI" |
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```bash |
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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 YOLO11n-obb model to run predictions on images. |
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!!! example |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a 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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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image |
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``` |
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=== "CLI" |
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```bash |
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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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<p align="center"> |
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<br> |
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/5XYdm5CYODA" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> How to Detect and Track Storage Tanks using Ultralytics YOLO-OBB | Oriented Bounding Boxes | DOTA |
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</p> |
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See full `predict` mode details in the [Predict](../modes/predict.md) page. |
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## Export |
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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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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a 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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model.export(format="onnx") |
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``` |
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=== "CLI" |
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```bash |
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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 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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See full `export` details in the [Export](../modes/export.md) page. |
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## FAQ |
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### What are Oriented Bounding Boxes (OBB) and how do they differ from regular bounding boxes? |
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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 YOLO11n-obb model using a custom dataset? |
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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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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained model |
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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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``` |
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=== "CLI" |
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```bash |
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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 YOLO11-OBB models? |
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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 YOLO11-OBB model to ONNX format? |
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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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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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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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``` |
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=== "CLI" |
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```bash |
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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 YOLO11n-obb model? |
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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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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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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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``` |
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=== "CLI" |
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```bash |
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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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