Yolov5 for Oriented Object Detection

The code for the implementation of “yolov5 + circular Smooth Label”.

Results and Models

The results on DOTAv1.5_subsize1024_gap200_rate1.0 test-dev set are shown in the table below. (password:yolo)

Model (link)	Size (pixels)	TTA (multi-scale/ rotate testing)	mAPOBB 0.5	Speed CPU b1 (ms)	Speed 2080Ti b1 (ms)	params (M)	FLOPs @1024 (B)
yolov5m	1024	×	73.19	-	-	21.6	50.5
yolov5m6	1024	×	-	-	-	-	-
yolov5m7	1024	×	-	-	-	-	-

Note:

• All the pre-trained checkpoint about yolov5 can be downloaded in this link.

Updates

• [2022/1/7] : Faster and stronger, some bugs fixed

Installation

Please refer to for installation and dataset preparation.

Getting Started

This repo is based on . Please see for the Oriented Detection basic usage.

Acknowledgements

I have used utility functions from other wonderful open-source projects. Espeicially thank the authors of:

• ultralytics/yolov5.
• Thinklab-SJTU/CSL_RetinaNet_Tensorflow.
• jbwang1997/OBBDetection
• CAPTAIN-WHU/DOTA_devkit

More detailed explanation

想要了解相关实现的细节和原理可以看我的知乎文章:

• 自己改建YOLOv5旋转目标的踩坑记录.

有问题反馈

在使用中有任何问题，欢迎反馈给我，可以用以下联系方式跟我交流

• 知乎（@略略略）
• 代码问题提issues,其他问题请知乎上联系

关于作者

  Name  : "胡凯旋"
  describe myself："咸鱼一枚"