| --- |
| comments: true |
| 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 |
| --- |
| |
| # Oriented Bounding Boxes Object Detection |
|
|
| <!-- 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. |
|
|
| <!-- youtube video link for obb task --> |
|
|
| !!! 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). |
| |
| <table> |
| <tr> |
| <td align="center"> |
| <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Z7Z9pHF8wJc" |
| title="YouTube video player" frameborder="0" |
| allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
| allowfullscreen> |
| </iframe> |
| <br> |
| <strong>Watch:</strong> Object Detection using Ultralytics YOLOv8 Oriented Bounding Boxes (YOLOv8-OBB) |
| </td> |
| <td align="center"> |
| <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/uZ7SymQfqKI" |
| title="YouTube video player" frameborder="0" |
| allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
| allowfullscreen> |
| </iframe> |
| <br> |
| <strong>Watch:</strong> Object Detection with YOLOv8-OBB using Ultralytics HUB |
| </td> |
| </tr> |
| </table> |
| |
| ## Visual Samples |
|
|
| | Ships Detection using OBB | Vehicle Detection using OBB | |
| | :------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------: | |
| |  |  | |
|
|
| ## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8) |
|
|
| 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. |
|
|
| [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. |
|
|
| | Model | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) | |
| | -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | |
| | [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-obb.pt) | 1024 | 78.0 | 204.77 | 3.57 | 3.1 | 23.3 | |
| | [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-obb.pt) | 1024 | 79.5 | 424.88 | 4.07 | 11.4 | 76.3 | |
| | [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-obb.pt) | 1024 | 80.5 | 763.48 | 7.61 | 26.4 | 208.6 | |
| | [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-obb.pt) | 1024 | 80.7 | 1278.42 | 11.83 | 44.5 | 433.8 | |
| | [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-obb.pt) | 1024 | 81.36 | 1759.10 | 13.23 | 69.5 | 676.7 | |
|
|
| - **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). |
| - **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` |
|
|
| ## Train |
|
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| Train YOLOv8n-obb on the `dota8.yaml` dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page. |
|
|
| !!! example |
|
|
| === "Python" |
| |
| ```python |
| from ultralytics import YOLO |
| |
| # Load a model |
| model = YOLO("yolov8n-obb.yaml") # build a new model from YAML |
| model = YOLO("yolov8n-obb.pt") # load a pretrained model (recommended for training) |
| model = YOLO("yolov8n-obb.yaml").load("yolov8n.pt") # build from YAML and transfer weights |
| |
| # Train the model |
| results = model.train(data="dota8.yaml", epochs=100, imgsz=640) |
| ``` |
| |
| === "CLI" |
| |
| ```bash |
| # Build a new model from YAML and start training from scratch |
| yolo obb train data=dota8.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640 |
| |
| # Start training from a pretrained *.pt model |
| yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640 |
| |
| # Build a new model from YAML, transfer pretrained weights to it and start training |
| yolo obb train data=dota8.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640 |
| ``` |
| |
| ### 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 YOLOv8n-obb model accuracy on the DOTA8 dataset. No argument need to passed as the `model` |
| retains its training `data` and arguments as model attributes. |
|
|
| !!! example |
|
|
| === "Python" |
| |
| ```python |
| from ultralytics import YOLO |
| |
| # Load a model |
| model = YOLO("yolov8n-obb.pt") # load an official model |
| model = YOLO("path/to/best.pt") # load a custom model |
| |
| # Validate the model |
| metrics = model.val(data="dota8.yaml") # no arguments needed, dataset and settings remembered |
| metrics.box.map # map50-95(B) |
| metrics.box.map50 # map50(B) |
| metrics.box.map75 # map75(B) |
| metrics.box.maps # a list contains map50-95(B) of each category |
| ``` |
| |
| === "CLI" |
| |
| ```bash |
| yolo obb val model=yolov8n-obb.pt data=dota8.yaml # val official model |
| yolo obb val model=path/to/best.pt data=path/to/data.yaml # val custom model |
| ``` |
| |
| ## Predict |
|
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| Use a trained YOLOv8n-obb model to run predictions on images. |
|
|
| !!! example |
|
|
| === "Python" |
| |
| ```python |
| from ultralytics import YOLO |
| |
| # Load a model |
| model = YOLO("yolov8n-obb.pt") # load an official model |
| model = YOLO("path/to/best.pt") # load a custom model |
| |
| # Predict with the model |
| results = model("https://ultralytics.com/images/bus.jpg") # predict on an image |
| ``` |
| |
| === "CLI" |
| |
| ```bash |
| yolo obb predict model=yolov8n-obb.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model |
| yolo obb predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model |
| ``` |
| |
| 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. |
|
|
| !!! example |
|
|
| === "Python" |
| |
| ```python |
| from ultralytics import YOLO |
| |
| # Load a model |
| model = YOLO("yolov8n-obb.pt") # load an official model |
| model = YOLO("path/to/best.pt") # load a custom trained model |
| |
| # Export the model |
| model.export(format="onnx") |
| ``` |
| |
| === "CLI" |
| |
| ```bash |
| yolo export model=yolov8n-obb.pt format=onnx # export official model |
| yolo export model=path/to/best.pt format=onnx # export custom trained model |
| ``` |
| |
| 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. |
|
|
| {% include "macros/export-table.md" %} |
|
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| See full `export` details in the [Export](../modes/export.md) page. |
|
|
| ## 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 YOLOv8n-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: |
|
|
| !!! example |
|
|
| === "Python" |
| |
| ```python |
| from ultralytics import YOLO |
| |
| # Load a pretrained model |
| model = YOLO("yolov8n-obb.pt") |
| |
| # Train the model |
| results = model.train(data="path/to/custom_dataset.yaml", epochs=100, imgsz=640) |
| ``` |
| |
| === "CLI" |
| |
| ```bash |
| yolo obb train data=path/to/custom_dataset.yaml model=yolov8n-obb.pt epochs=100 imgsz=640 |
| ``` |
| |
| For more training arguments, check the [Configuration](../usage/cfg.md) section. |
|
|
| ### What datasets can I use for training YOLOv8-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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| ### How can I export a YOLOv8-OBB model to ONNX format? |
|
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| Exporting a YOLOv8-OBB model to ONNX format is straightforward using either Python or CLI: |
|
|
| !!! example |
|
|
| === "Python" |
| |
| ```python |
| from ultralytics import YOLO |
| |
| # Load a model |
| model = YOLO("yolov8n-obb.pt") |
| |
| # Export the model |
| model.export(format="onnx") |
| ``` |
| |
| === "CLI" |
| |
| ```bash |
| yolo export model=yolov8n-obb.pt format=onnx |
| ``` |
| |
| For more export formats and details, refer to the [Export](../modes/export.md) page. |
|
|
| ### How do I validate the accuracy of a YOLOv8n-obb model? |
|
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| To validate a YOLOv8n-obb model, you can use Python or CLI commands as shown below: |
|
|
| !!! example |
|
|
| === "Python" |
| |
| ```python |
| from ultralytics import YOLO |
| |
| # Load a model |
| model = YOLO("yolov8n-obb.pt") |
| |
| # Validate the model |
| metrics = model.val(data="dota8.yaml") |
| ``` |
| |
| === "CLI" |
| |
| ```bash |
| yolo obb val model=yolov8n-obb.pt data=dota8.yaml |
| ``` |
| |
| See full validation details in the [Val](../modes/val.md) section. |
|
|
