README.md

CrowdCount-Cascaded-mtl

input

Ailia input shape: (1, 1, 480, 640)

output

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 crowdcount-cascaded-mtl.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 crowdcount-cascaded-mtl.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 crowdcount-cascaded-mtl.py --video VIDEO_PATH --savepath SAVE_VIDEO_PATH

Reference

CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting (Single Image Crowd Counting)

Framework

PyTorch 1.3

Model Format

ONNX opset = 10

Netron

crowdcount.onnx.prototxt