--- 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 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 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 | | :------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------: | | ![Ships Detection using OBB](https://github.com/ultralytics/docs/releases/download/0/ships-detection-using-obb.avif) | ![Vehicle Detection using OBB](https://github.com/ultralytics/docs/releases/download/0/vehicle-detection-using-obb.avif) | ## [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
(pixels) | mAPtest
50 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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 | - **mAPtest** values are for single-model multiscale on [DOTAv1 test](https://captain-whu.github.io/DOTA/index.html) dataset.
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.
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 arguments are needed 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](../modes/predict.md) 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. {% include "macros/export-table.md" %} See full `export` details in the [Export](../modes/export.md) 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](../datasets/obb/index.md)). ### 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](../usage/cfg.md) section. ### What datasets can I use for training YOLOv8-OBB models? 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). ### 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](../modes/export.md) 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](../modes/val.md) section.