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

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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.

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 "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).

<p align="center">
  <br>
  <iframe 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)
</p>

## Visual Samples

|                                                    Ships Detection using OBB                                                    |                                                    Vehicle Detection using OBB                                                    |
|:-------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------:|
| ![Ships Detection using OBB](https://github.com/RizwanMunawar/ultralytics/assets/62513924/5051d324-416f-4b58-ab62-f1bf9d7134b0) | ![Vehicle Detection using OBB](https://github.com/RizwanMunawar/ultralytics/assets/62513924/9a366262-910a-403b-a5e2-9c68b75700d3) |

## [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/v8.1.0/yolov8n-obb.pt) | 1024                  | 78.0               | 204.77                         | 3.57                                | 3.1                | 23.3              |
| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8s-obb.pt) | 1024                  | 79.5               | 424.88                         | 4.07                                | 11.4               | 76.3              |
| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8m-obb.pt) | 1024                  | 80.5               | 763.48                         | 7.61                                | 26.4               | 208.6             |
| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-obb.pt) | 1024                  | 80.7               | 1278.42                        | 11.83                               | 44.5               | 433.8             |
| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-obb.pt) | 1024                  | 81.36              | 1759.10                        | 13.23                               | 69.5               | 676.7             |

- **mAP<sup>test</sup>** values are for single-model multi-scale 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

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

OBB dataset format can be found in detail in the [Dataset Guide](../datasets/obb/index.md).

## 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](https://docs.ultralytics.com/modes/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](https://pytorch.org/)                                    | -                 | `yolov8n-obb.pt`              | ✅        | -                                                   |
| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n-obb.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n-obb.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n-obb_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n-obb.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n-obb.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n-obb_saved_model/`    | ✅        | `imgsz`, `keras`                                    |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n-obb.pb`              | ❌        | `imgsz`                                             |
| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n-obb.tflite`          | ✅        | `imgsz`, `half`, `int8`                             |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n-obb_edgetpu.tflite`  | ✅        | `imgsz`                                             |
| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n-obb_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n-obb_paddle_model/`   | ✅        | `imgsz`                                             |
| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n-obb_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |

See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.
