yolov

YOLOV : for video object detection.

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/)

Ailia input shape: (1, 3, 576, 576)

Output

This model cannot detect person. (Label list : https://image-net.org/challenges/LSVRC/2015/browse-vid-synsets.php)

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 yolov.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 yolov.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 yolov.py --video VIDEO_PATH

By adding the --model option, you can specify model type which is selected from "yolov_s", "yolov_l", "yolov_x".

$ python3 yolov.py --model MODELNAME

Reference

YOLOV

Framework

Pytorch

Model Format

ONNX opset = 13

Netron

yolov_s.onnx.prototxt

yolov_l.onnx.prototxt

yolov_x.onnx.prototxt