Yolov5 for Oriented Object Detection csl and kld

The code for the implementation of “Yolov5 + Circular Smooth Label +KLD”.

python val.py --data 'data/dotav15_poly.yaml' --img 1024 --conf 0.01 --iou 0.4 --task 'test' --batch 16 --save-json --name 'dotav15_test_split'
python tools/TestJson2VocClassTxt.py --json_path 'runs/val/dotav15_test_split/best_obb_predictions.json' --save_path 'runs/val/dotav15_test_split/obb_predictions_Txt'
python DOTA_devkit/ResultMerge_multi_process.py --scrpath 'runs/val/dotav15_test_split/obb_predictions_Txt' --dstpath 'runs/val/dotav15_test_split/obb_predictions_Txt_Merged'
zip the poly format results files and submit it to https://captain-whu.github.io/DOTA/evaluation.html
python train.py --loss_fun ('CSL' or 'KLD')

• 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

Installation

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

Getting Started

This repo is based on yolov5.

And this repo has been rebuilt, Please see GetStart.md for the Oriented Detection latest 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
• lx-cly/YOLOv5_OBB_KLD