Models

What is YOLOv5 Oriented Bounding Boxes?

YOLOv5-OBB is a variant of YOLOv5 that supports oriented bounding boxes. This model is designed to yield predictions that better fit objects that are positioned at an angle.

About the model

Here is an overview of the

YOLOv5 Oriented Bounding Boxes

model:

Date of Release Mar 12, 2020
Model Type Object Detection
Architecture CNN, YOLO
Framework Used PyTorch
Annotation Format YOLOv5 Oriented Bounding Boxes
Stars on GitHub 33200+

What is YOLOv5-OBB?

Oriented bounding boxes are bounding boxes rotated to better fit the objects represented on an angle. Take a pill detection dataset for example. Using YOLOv5-OBB we are able to detect pills that are rotated on a given frame or image more tightly and accurately, preventing capture of multiple pills or other objects in one bounding box.

What is YOLOv5-OBB?

YOLOv5-OBB with American Sign Language

YOLOv5-OBB with American Sign Language

Further Reading

Getting Started: https://github.com/hukaixuan19970627/yolov5_obb/blob/master/docs/GetStart.md
YOLOv5-OBB Paper: https://arxiv.org/abs/2003.05597v2
Code for ECCV 2020 paper: Arbitrary-Oriented Object Detection with Circular Smooth Label: https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow

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Model Performance

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YOLOv5 Oriented Bounding Boxes Annotation Format

YOLOv5 Oriented Bounding Boxes

uses the

YOLOv5 Oriented Bounding Boxes

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.

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