Yolov5 for Oriented Object Detection

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

Results and Models

The results on DOTA_subsize1024_gap200_rate1.0 test-dev set are shown in the table below. (password: yolo)

• All checkpoints are trained to 300 epochs with COCO pre-trained checkpoints, default settings and hyperparameters.
• mAPtest dota values are for single-model single-scale on DOTA(1024,1024,200,1.0) dataset.Reproduce Example:

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

• 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, yolov5 base version updated.

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

More detailed explanation

:

• YOLOv5.

install.mdGetStart.mdgithubissue

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