The required .names file and .txt files were created. The training and test is however carried on image size much larger than the respective one in YOLOv3 i.e. 608 x 608 and hence the training and test time is comparatively greater by almost 50 percent, for the same dataset. This too has good visualization with Tensorboard.

Pytorch-YOLOv4

├── 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_annotation.py       coco dataset generator
│   ├── config.py
│   ├── darknet2pytorch.py
│   ├── region_loss.py
│   ├── utils.py
│   └── yolo_layer.py

0 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

1. Download weight

2. 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 ...
   ...
   ...
   

3. Train

   you can set parameters in cfg.py.

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

2 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, ...}

• Load pytorch weights (pth file) to do the inference

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

3 Inference output

There are 2 inference outputs.

• One is locations of bounding boxes, its shape is [batch, num_boxes, 1, 4] which represents x1, y1, x2, y2 of each bounding box.
• The other one is scores of bounding boxes which is of shape [batch, num_boxes, num_classes] indicating scores of all classes for each bounding box.

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

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}
}