ultralytics/docs/en/tasks/obb.md

comments	description	keywords
true	Learn how to use oriented object detection models with Ultralytics YOLO. Instructions on training, validation, image prediction, and model export.	yolov8, oriented object detection, Ultralytics, DOTA dataset, rotated object detection, object detection, model training, model validation, image prediction, model export

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)

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 multi-scale 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 it's 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 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
ONNX	onnx	yolov8n-obb.onnx	✅	imgsz, half, dynamic, simplify, opset
OpenVINO	openvino	yolov8n-obb_openvino_model/	✅	imgsz, half, int8
TensorRT	engine	yolov8n-obb.engine	✅	imgsz, half, dynamic, simplify, workspace
CoreML	coreml	yolov8n-obb.mlpackage	✅	imgsz, half, int8, nms
TF SavedModel	saved_model	yolov8n-obb_saved_model/	✅	imgsz, keras
TF GraphDef	pb	yolov8n-obb.pb	❌	imgsz
TF Lite	tflite	yolov8n-obb.tflite	✅	imgsz, half, int8
TF Edge TPU	edgetpu	yolov8n-obb_edgetpu.tflite	✅	imgsz
TF.js	tfjs	yolov8n-obb_web_model/	✅	imgsz, half, int8
PaddlePaddle	paddle	yolov8n-obb_paddle_model/	✅	imgsz
ncnn	ncnn	yolov8n-obb_ncnn_model/	✅	imgsz, half

See full export details in the Export page.