yolov4

yolov4

Input

(Image from https://pixabay.com/ja/photos/%E3%83%AD%E3%83%B3%E3%83%89%E3%83%B3%E5%B8%82-%E9%8A%80%E8%A1%8C-%E3%83%AD%E3%83%B3%E3%83%89%E3%83%B3-4481399/)

Shape : (1, 3, 416, 416)
Range : [0.0, 1.0]

Output

• ouput1 shape : (batch, num, 1, position)
• ouput2 shape : (batch, num, category_probability)
• batch : 1
• num : 10647
• category_probability : [probability, ] * 80
• probability : [0.0,1.0]
• position : left, top, right, bottom [0,1]

usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 yolov4.py

If you want to specify the input image, put the image path after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 yolov4.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.

$ python3 yolov4.py --video VIDEO_PATH

Reference

• YOLO: Real-Time Object Detection
• Pytorch-YOLOv4

Framework

Pytorch

Model Format

ONNX opset=10

Netron

yolov4.onnx.prototxt