Pytorch-YOLOv4

A minimal PyTorch implementation of YOLOv4.

Paper Yolo v4: https://arxiv.org/abs/2004.10934
Source code:https://github.com/AlexeyAB/darknet
More details: http://pjreddie.com/darknet/yolo/
Inference
Train
- Mocaic

├── README.md
├── dataset.py       dataset
├── demo.py          demo to run pytorch --> tool/darknet2pytorch
├── darknet2onnx.py  tool to convert into onnx --> tool/darknet2pytorch
├── demo_onnx.py     demo to run the converted onnx model
├── models.py        model for pytorch
├── train.py         train models.py
├── cfg.py           cfg.py for train
├── cfg              cfg --> darknet2pytorch
├── data            
├── weight           --> darknet2pytorch
├── tool
│   ├── camera.py           a demo camera
│   ├── coco_annotatin.py       coco dataset generator
│   ├── config.py
│   ├── darknet2pytorch.py
│   ├── region_loss.py
│   ├── utils.py
│   └── yolo_layer.py

0. Weight

0.1 darkent

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.

baidu
- yolov4.pth(https://pan.baidu.com/s/1ZroDvoGScDgtE1ja_QqJVw Extraction code:xrq9)
- yolov4.conv.137.pth(https://pan.baidu.com/s/1ovBie4YyVQQoUrC3AY0joA Extraction code:kcel)
google
- yolov4.pth(https://drive.google.com/open?id=1wv_LiFeCRYwtpkqREPeI13-gPELBDwuJ)
- yolov4.conv.137.pth(https://drive.google.com/open?id=1fcbR0bWzYfIEdLJPzOsn4R5mlvR6IQyA)

1. Train

use yolov4 to train your own data

Download weight

Transform data

For coco dataset,you can use tool/coco_annotatin.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 ...
...
...

Train

you can set parameters in cfg.py.

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

2. Inference

download model weight https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT

python demo.py <cfgFile> <weightFile> <imgFile>

3. Darknet2ONNX (Evolving)

Pytorch version Recommended: 1.4.0
Install onnxruntime
```
pip install onnxruntime
```
Run python script to generate onnx model and run the demo
```
python demo_onnx.py <cfgFile> <weightFile> <imageFile> <batchSize>
```
This script will generate 2 onnx models.
- One is for running the demo (batch_size=1)
- The other one is what you want to generate (batch_size=batchSize)

4. ONNX2TensorRT (Evolving)

TensorRT version Recommended: 7.0, 7.1
Run the following command to convert VOLOv4 onnx model into TensorRT engine
```
trtexec --onnx=<onnx_file> --explicitBatch --saveEngine=<tensorRT_engine_file> --workspace=<size_in_megabytes> --fp16
```
- Note: If you want to use int8 mode in conversion, extra int8 calibration is needed.
Run the demo (this demo here only works when batchSize=1)
```
python demo_trt.py <tensorRT_engine_file> <input_image> <input_H> <input_W>
```
- Note1: input_H and input_W should agree with the input size in the original darknet cfg file as well as the latter onnx file.
- Note2: extra NMS operations are needed for the tensorRT ouput. This demo uses TianXiaomo's NMS code from tool/utils.py.

5. ONNX2Tensorflow

First:Conversion to ONNX

tensorflow >=2.0

1: Thanks:github:https://github.com/onnx/onnx-tensorflow

2: Run git clone https://github.com/onnx/onnx-tensorflow.git && cd onnx-tensorflow Run pip install -e .

Note:Errors will occur when using "pip install onnx-tf", at least for me,it is recommended to use source code installation

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}
}

xu-peng-tao/pytorch-YOLOv4