yolov4_pytorch's Introduction

yolov4 pytorch in my program

项目中用来训练闸机的行人检测以及行人跟踪

个人代码注释以及解读说明，使用的方法和函数

0. Weights Download

0.1 darknet

baidu(https://pan.baidu.com/s/1dAGEW8cm-dqK14TbhhVetA Extraction code:dm5b)
google(https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT)

0.2 pytorch

you can use darknet2pytorch to convert it yourself, or download my converted model.

1. Train

use yolov4 to train your own data for training darknet version see https://github.com/AlexeyAB/darknet

Download weight

Transform data

For coco dataset,you can use tool/coco_annotation.py.

# train.txt
image_path1 x1,y1,x2,y2,id x1,y1,x2,y2,id x1,y1,x2,y2,id ...
image_path2 x1,y1,x2,y2,id x1,y1,x2,y2,id x1,y1,x2,y2,id ...
...
...

change get_image_id function in dataset.py for evaluating

Train

you can set parameters in cfg.py.

 python train.py -g [GPU_ID] -dir [Dataset direction] ...

2. Inference

2.3 Different inference options

Load the pretrained darknet model and darknet weights to do the inference (image size is configured in cfg file already)
```
python demo.py -cfgfile <cfgFile> -weightfile <weightFile> -imgfile <imgFile>
```

Load pytorch weights (pth file) to do the inference

python models.py <num_classes> <weightfile> <imgfile> <IN_IMAGE_H> <IN_IMAGE_W> <namefile(optional)>

Reference:

@article{yolov4,
  title={YOLOv4: YOLOv4: Optimal Speed and Accuracy of Object Detection},
  author={Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao},
  journal = {arXiv},
  year={2020}
}

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