Skip to content

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.


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 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
  • mAPtest values are for single-model multiscale 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

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)
# 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 its training data and arguments as model attributes.

Example

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
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

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
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

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")
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 - yolov8n-obb.pt -
TorchScript torchscript yolov8n-obb.torchscript imgsz, optimize, batch
ONNX onnx yolov8n-obb.onnx imgsz, half, dynamic, simplify, opset, batch
OpenVINO openvino yolov8n-obb_openvino_model/ imgsz, half, int8, batch
TensorRT engine yolov8n-obb.engine imgsz, half, dynamic, simplify, workspace, int8, batch
CoreML coreml yolov8n-obb.mlpackage imgsz, half, int8, nms, batch
TF SavedModel saved_model yolov8n-obb_saved_model/ imgsz, keras, int8, batch
TF GraphDef pb yolov8n-obb.pb imgsz, batch
TF Lite tflite yolov8n-obb.tflite imgsz, half, int8, batch
TF Edge TPU edgetpu yolov8n-obb_edgetpu.tflite imgsz
TF.js tfjs yolov8n-obb_web_model/ imgsz, half, int8, batch
PaddlePaddle paddle yolov8n-obb_paddle_model/ imgsz, batch
NCNN ncnn yolov8n-obb_ncnn_model/ imgsz, half, batch

See full export details in the Export page.



Created 2024-01-05, Updated 2024-06-10
Authors: glenn-jocher (20), Burhan-Q (4), Laughing-q (3), AyushExel (1)

Comments