yolov2

yolov2

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 : [-1.0, 1.0]

Output

• category : [0,79]
• probablity : [0.0,1.0]
• position : x, y, w, h [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 yolov2.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 yolov2.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 yolov2.py --video VIDEO_PATH

Reference

• YOLO: Real-Time Object Detection
• Covert original YOLO model from Pytorch to Onnx, and do inference using backend Caffe2 or Tensorflow.

Framework

Pytorch 1.3.1

Model Format

ONNX opset=10

Netron

yolov2.onnx.prototxt