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 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).
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.
- **mAP<sup>test</sup>** values are for single-model multi-scale on [DOTAv1 test](http://cocodataset.org) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test`
- **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 YOLOv8n-obb on the dota128.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
Validate trained YOLOv8n-obb model accuracy on the dota128-obb 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() # 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 # val official model
yolo obb val model=path/to/best.pt # 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 |
