90 lines
4.8 KiB
Markdown
90 lines
4.8 KiB
Markdown
# TPH-YOLOv5
|
|
This repo is the implementation of ["TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios"](https://openaccess.thecvf.com/content/ICCV2021W/VisDrone/html/Zhu_TPH-YOLOv5_Improved_YOLOv5_Based_on_Transformer_Prediction_Head_for_Object_ICCVW_2021_paper.html) and ["TPH-YOLOv5++: Boosting Object Detection on Drone-Captured Scenarios with Cross-Layer Asymmetric Transformer"](https://www.mdpi.com/2072-4292/15/6/1687).
|
|
On [VisDrone Challenge 2021](http://aiskyeye.com/), 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](https://openaccess.thecvf.com/content/ICCV2021W/VisDrone/html/Cao_VisDrone-DET2021_The_Vision_Meets_Drone_Object_Detection_Challenge_Results_ICCVW_2021_paper.html) 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
|
|
```bash
|
|
$ 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.
|
|
```bash
|
|
$ python VisDrone2YOLO_lable.py
|
|
```
|
|
|
|
# Inference
|
|
* `Datasets` : [VisDrone](http://aiskyeye.com/download/object-detection-2/), [UAVDT](https://sites.google.com/view/grli-uavdt/%E9%A6%96%E9%A1%B5)
|
|
* `Weights` (PyTorch
|
|
v1.10):
|
|
* `yolov5l-xs-1.pt`: | [Baidu Drive(pw: vibe)](https://pan.baidu.com/s/1APETgMoeCOvZi1GsBZERrg). | [Google Drive](https://drive.google.com/file/d/1nGeKl3qOa26v3haGSDmLjeA0cjDD9p61/view?usp=sharing) |
|
|
* `yolov5l-xs-2.pt`: | [Baidu Drive(pw: vffz)](https://pan.baidu.com/s/19S84EevP86yJIvnv9KYXDA). | [Google Drive](https://drive.google.com/file/d/1VmORvxNtvMVMvmY7cCwvp0BoL6L3RGiq/view?usp=sharing) |
|
|
|
|
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.)
|
|
|
|
```bash
|
|
$ 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.
|
|
```bash
|
|
$ python wbf.py
|
|
```
|
|
|
|
# Train
|
|
train.py allows you to train new model from strach.
|
|
```bash
|
|
$ 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
|
|
* [ultralytics/yolov5](https://github.com/ultralytics/yolov5)
|
|
* [SwinTransformer](https://github.com/microsoft/Swin-Transformer)
|
|
* [WBF](https://github.com/ZFTurbo/Weighted-Boxes-Fusion)
|