Code for Crowd counting via unsupervised cross-domain feature adaptation

Dec 17, 2021 1 min read

CDFA-pytorch

Code for Unsupervised crowd counting via cross-domain feature adaptation.

Pre-trained models

Google Drive

Baidu Cloud : t4qc

Environment

We are good in the environment:

python 3.6

CUDA 9.2

Pytorch 1.2.0

numpy 1.19.2

matplotlib 3.3.4

Usage

We provide the test code for our model. The result_gcc_qnrf.pth model is adapted from the GCC dataset to the UCF_QNRF dataset. We randomly select an image from the UCF_QNRF dataset and place it in the image folder. And you can either choose the other images for a test.

We are good to run:

python test.py --model CDFA --model_state ./model/result_gcc_qnrf.pth --out ./out/out.png

We will release more pre-trained models soon. Please see the paper for more details.

Citation

Coming soon

Acknowledgement

Thanks to these repositories

Unsupervised Domain

John was the first writer to have joined pythonawesome.com. He has since then inculcated very effective writing and reviewing culture at pythonawesome which rivals have found impossible to imitate.