296 lines
15 KiB
Markdown
296 lines
15 KiB
Markdown
---
|
|
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
|
|
---
|
|
|
|
# Oriented Bounding Boxes Object Detection
|
|
|
|
<!-- obb task poster -->
|
|
|
|
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).
|
|
|
|
<table>
|
|
<tr>
|
|
<td align="center">
|
|
<iframe loading="lazy" 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)
|
|
</td>
|
|
<td align="center">
|
|
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/uZ7SymQfqKI"
|
|
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 with YOLOv8-OBB using Ultralytics HUB
|
|
</td>
|
|
</tr>
|
|
</table>
|
|
|
|
## Visual Samples
|
|
|
|
| Ships Detection using OBB | Vehicle Detection using OBB |
|
|
| :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------: |
|
|
|  |  |
|
|
|
|
## [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/v8.2.0/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 |
|
|
|
|
- **mAP<sup>test</sup>** values are for single-model multiscale 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 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.
|
|
|
|
| Format | `format` Argument | Model | Metadata | Arguments |
|
|
| ------------------------------------------------- | ----------------- | ----------------------------- | -------- | -------------------------------------------------------------------- |
|
|
| [PyTorch](https://pytorch.org/) | - | `yolov8n-obb.pt` | ✅ | - |
|
|
| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-obb.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
|
|
| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-obb.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
|
|
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-obb_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
|
|
| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-obb.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
|
|
| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-obb.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
|
|
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-obb_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
|
|
| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-obb.pb` | ❌ | `imgsz`, `batch` |
|
|
| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-obb.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
|
|
| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-obb_edgetpu.tflite` | ✅ | `imgsz` |
|
|
| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-obb_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
|
|
| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-obb_paddle_model/` | ✅ | `imgsz`, `batch` |
|
|
| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-obb_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
|
|
|
|
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.
|