ultralytics/docs/en/tasks/obb.md

comments	description	keywords
true	Discover how to detect objects with rotation for higher precision using YOLOv8 OBB models. Learn, train, validate, and export OBB models effortlessly.	Oriented Bounding Boxes, OBB, Object Detection, YOLOv8, Ultralytics, DOTAv1, Model Training, Model Export, AI, Machine Learning

Oriented Bounding Boxes Object Detection

Oriented object detection goes a step further than object detection and introduce an extra angle to locate objects more accurate in an image.

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.

!!! Tip "Tip"

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).

Watch: Object Detection using Ultralytics YOLOv8 Oriented Bounding Boxes (YOLOv8-OBB)

Watch: Object Detection with YOLOv8-OBB using Ultralytics HUB

Visual Samples

Ships Detection using OBB	Vehicle Detection using OBB

Models

YOLOv8 pretrained OBB models are shown here, which are pretrained on the DOTAv1 dataset.

Models download automatically from the latest Ultralytics release on first use.

Model	size (pixels)	mAPtest 50	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n-obb	1024	78.0	204.77	3.57	3.1	23.3
YOLOv8s-obb	1024	79.5	424.88	4.07	11.4	76.3
YOLOv8m-obb	1024	80.5	763.48	7.61	26.4	208.6
YOLOv8l-obb	1024	80.7	1278.42	11.83	44.5	433.8
YOLOv8x-obb	1024	81.36	1759.10	13.23	69.5	676.7

• mAP^test values are for single-model multiscale on DOTAv1 test dataset.
  Reproduce by yolo val obb data=DOTAv1.yaml device=0 split=test and submit merged results to DOTA evaluation.
• Speed averaged over DOTAv1 val images using an Amazon EC2 P4d instance.
  Reproduce by yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu

Train

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 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

OBB dataset format can be found in detail in the Dataset Guide.

Val

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

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 page.

Export

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.

Format	format Argument	Model	Metadata	Arguments
PyTorch	-	yolov8n-obb.pt	✅	-
TorchScript	torchscript	yolov8n-obb.torchscript	✅	imgsz, optimize, batch
ONNX	onnx	yolov8n-obb.onnx	✅	imgsz, half, dynamic, simplify, opset, batch
OpenVINO	openvino	yolov8n-obb_openvino_model/	✅	imgsz, half, int8, batch
TensorRT	engine	yolov8n-obb.engine	✅	imgsz, half, dynamic, simplify, workspace, int8, batch
CoreML	coreml	yolov8n-obb.mlpackage	✅	imgsz, half, int8, nms, batch
TF SavedModel	saved_model	yolov8n-obb_saved_model/	✅	imgsz, keras, int8, batch
TF GraphDef	pb	yolov8n-obb.pb	❌	imgsz, batch
TF Lite	tflite	yolov8n-obb.tflite	✅	imgsz, half, int8, batch
TF Edge TPU	edgetpu	yolov8n-obb_edgetpu.tflite	✅	imgsz
TF.js	tfjs	yolov8n-obb_web_model/	✅	imgsz, half, int8, batch
PaddlePaddle	paddle	yolov8n-obb_paddle_model/	✅	imgsz, batch
NCNN	ncnn	yolov8n-obb_ncnn_model/	✅	imgsz, half, batch

See full export details in the Export page.

FAQ

What are Oriented Bounding Boxes (OBB) and how do they differ from regular bounding boxes?

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).

How do I train a YOLOv8n-obb model using a custom dataset?

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 section.

What datasets can I use for training YOLOv8-OBB models?

YOLOv8-OBB models are pretrained on datasets like DOTAv1 but you can use any dataset formatted for OBB. Detailed information on OBB dataset formats can be found in the Dataset Guide.

How can I export a YOLOv8-OBB model to ONNX format?

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 page.

How do I validate the accuracy of a YOLOv8n-obb model?

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 section.