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
├── demo_darknet2onnx.py  tool to convert into onnx --> tool/darknet2pytorch
├── demo_pytorch2onnx.py  tool to convert into onnx
├── 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

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

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)

use yolov4 to train your own data

1. Download weight

2. 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 ...
   ...
   ...
   

3. Train

   you can set parameters in cfg.py.

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

• Image input size for inference

  Image input size is NOT restricted in 320 * 320, 416 * 416, 512 * 512 and 608 * 608. You can adjust your input sizes for a different input ratio, for example: 320 * 608. Larger input size could help detect smaller targets, but may be slower and GPU memory exhausting.

  height = 320 + 96 * n, n in {0, 1, 2, 3, ...}
  width  = 320 + 96 * m, m in {0, 1, 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)>

  • Load converted ONNX file to do inference (See section 3 and 4)

  • Load converted TensorRT engine file to do inference (See section 5)

• Inference output

  Inference output is of shape [batch, num_boxes, 4 + num_classes] in which [batch, num_boxes, 4] is x_center, y_center, width, height of bounding boxes, and [batch, num_boxes, num_classes] is confidences of bounding box for all classes.

  Until now, still a small piece of post-processing including NMS is required. We are trying to minimize time and complexity of post-processing.

• This script is to convert the official pretrained darknet model into ONNX

• Pytorch version Recommended:

  • Pytorch 1.4.0 for TensorRT 7.0 and higher
  • Pytorch 1.5.0 and 1.6.0 for TensorRT 7.1.2 and higher

• Install onnxruntime

  pip install onnxruntime

• Run python script to generate ONNX model and run the demo

  python demo_darknet2onnx.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)

• You can convert your trained pytorch model into ONNX using this script

• Pytorch version Recommended:

  • Pytorch 1.4.0 for TensorRT 7.0 and higher
  • Pytorch 1.5.0 and 1.6.0 for TensorRT 7.1.2 and higher

• Install onnxruntime

  pip install onnxruntime

• Run python script to generate ONNX model and run the demo

  python demo_pytorch2onnx.py <weight_file> <image_path> <batch_size> <n_classes> <IN_IMAGE_H> <IN_IMAGE_W>

  For example:

  python demo_pytorch2onnx.py yolov4.pth dog.jpg 8 80 416 416

  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=batch_size)

• 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

  python demo_trt.py <tensorRT_engine_file> <input_image> <input_H> <input_W>

  • This demo here only works when batchSize=1, but you can update this demo a little for batched inputs.

  • Note1: input_H and input_W should agree with the input size in the original ONNX file.

  • Note2: extra NMS operations are needed for the tensorRT output. This demo uses python NMS code from tool/utils.py.

• 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:

• https://github.com/eriklindernoren/PyTorch-YOLOv3
• https://github.com/marvis/pytorch-caffe-darknet-convert
• https://github.com/marvis/pytorch-yolo3

@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}
}