Awesome
TPH-YOLOv5
This repo is the implementation of "TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios" and "TPH-YOLOv5++: Boosting Object Detection on Drone-Captured Scenarios with Cross-Layer Asymmetric Transformer".
On VisDrone Challenge 2021, TPH-YOLOv5 wins 4th place and achieves well-matched results with 1st place model.
You can get VisDrone-DET2021: The Vision Meets Drone Object Detection Challenge Results for more information. The TPH-YOLOv5++, as an improved version, significantly improves inference efficiency and reduces computational costs while maintaining detection performance compared to TPH-YOLOv5.
Install
$ git clone https://github.com/cv516Buaa/tph-yolov5
$ cd tph-yolov5
$ pip install -r requirements.txt
Convert labels
VisDrone2YOLO_lable.py transfer VisDrone annotiations to yolo labels.
You should set the path of VisDrone dataset in VisDrone2YOLO_lable.py first.
$ python VisDrone2YOLO_lable.py
Inference
Datasets
: VisDrone, UAVDTWeights
(PyTorch v1.10):yolov5l-xs-1.pt
: | Baidu Drive(pw: vibe). | Google Drive |yolov5l-xs-2.pt
: | Baidu Drive(pw: vffz). | Google Drive |
val.py runs inference on VisDrone2019-DET-val, using weights trained with TPH-YOLOv5.
(We provide two weights trained by two different models based on YOLOv5l.)
$ python val.py --weights ./weights/yolov5l-xs-1.pt --img 1996 --data ./data/VisDrone.yaml
yolov5l-xs-2.pt
--augment --save-txt --save-conf --task val --batch-size 8 --verbose --name v5l-xs
Inference on UAVDT is similar and results of TPH-YOLOv5++ on UAVDT are as follow:
Ensemble
If you inference dataset with different models, then you can ensemble the result by weighted boxes fusion using wbf.py.
You should set img path and txt path in wbf.py.
$ python wbf.py
Train
train.py allows you to train new model from strach.
$ python train.py --img 1536 --adam --batch 4 --epochs 80 --data ./data/VisDrone.yaml --weights yolov5l.pt --hy data/hyps/hyp.VisDrone.yaml --cfg models/yolov5l-xs-tph.yaml --name v5l-xs-tph
$ python train.py --img 1536 --adam --batch 4 --epochs 80 --data ./data/VisDrone.yaml --weights yolov5l.pt --hy data/hyps/hyp.VisDrone.yaml --cfg models/yolov5l-tph-plus.yaml --name v5l-tph-plus
Description of TPH-YOLOv5, TPH-YOLOv5++ and citations
- https://arxiv.org/abs/2108.11539
- https://openaccess.thecvf.com/content/ICCV2021W/VisDrone/html/Zhu_TPH-YOLOv5_Improved_YOLOv5_Based_on_Transformer_Prediction_Head_for_Object_ICCVW_2021_paper.html
- https://www.mdpi.com/2072-4292/15/6/1687
If you have any question, please discuss with me by sending email to lyushuchang@buaa.edu.cn or liubinghao@buaa.edu.cn
If you find this code useful please cite:
@InProceedings{Zhu_2021_ICCV,
author = {Zhu, Xingkui and Lyu, Shuchang and Wang, Xu and Zhao, Qi},
title = {TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {October},
year = {2021},
pages = {2778-2788}
}
@Article{rs15061687,
AUTHOR = {Zhao, Qi and Liu, Binghao and Lyu, Shuchang and Wang, Chunlei and Zhang, Hong},
TITLE = {TPH-YOLOv5++: Boosting Object Detection on Drone-Captured Scenarios with Cross-Layer Asymmetric Transformer},
JOURNAL = {Remote Sensing},
VOLUME = {15},
YEAR = {2023},
NUMBER = {6},
ARTICLE-NUMBER = {1687},
URL = {https://www.mdpi.com/2072-4292/15/6/1687},
ISSN = {2072-4292},
DOI = {10.3390/rs15061687}
}
References
Thanks to their great works