Yolov5 for Oriented Object Detection Improved by BDAM

The code for the implementation of “Yolov5 + [BDAM]+ 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:0620)

Model (link)	Size (pixels)	TTA (multi-scale/ rotate testing)	OBB mAPtest 0.5 DOTAv1.5	Speed CPU b1 (ms)	Speed 2080Ti b1 (ms)	Speed 2080Ti b16 (ms)	params (M)	FLOPs @1024 (B)
yolov5x_bdam8 [baidu/google]	1024	×	74.32	-	-	-	-	-

The results on DIOR-R are shown in the table below.(password: 0620)

Model (link)	Size (pixels)	TTA (multi-scale/ rotate testing)	OBB mAPtest 0.5 DIOR-R	Speed CPU b1 (ms)	Speed 2080Ti b1 (ms)	Speed 2080Ti b16 (ms)	params (M)	FLOPs @1024 (B)
yolov5l_bdam5 [baidu/google]	1024	×	70.60	-	-	-	-	-

Table Notes (click to expand)

• All checkpoints are trained to 300 epochs with COCO pre-trained checkpoints, default settings and hyperparameters.
• mAP^test values are for single-model single-scale on DOTAv1.5 dataset.
  Reproduce by python val.py --data 'data/dotav15_poly.yaml' --img 1024 --conf 0.01 --iou 0.4 --task 'test' --batch 16 --save-json
• Speed averaged over DOTAv1.5 val_split_subsize1024_gap200 images using a 2080Ti gpu. NMS + pre-process times is included.
  Reproduce by python val.py --data 'data/dotav15_poly.yaml' --img 1024 --task speed --batch 1

Updates

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

Installation

Please refer to install.md for installation and dataset preparation.

Getting Started

This repo is based on yolov5. Please see GetStart.md 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
• hukaixuan19970627/yolov5_obb

关于作者

  Name  : "杨刚"
  describe myself："菜鸟一枚"