This is the official implementation of CSTrans. This repo is created by Zhongyi Huang.
The dataset can be downloaded from here: https://drive.google.com/file/d/1ymDYrGs9DSRicfZbSCDiOu0ikGDh5k6S/view?usp=sharing.
The precomputed density maps can be found here: https://archive.org/details/FSC147-GT.
The whole project directory structure is:
PROJECT_DIR
|--datasets
|--FSC-147
|--annotation_FSC-147.json
|--gt_density_map_adaptive
|--gt_density_map_adaptive_vis
|--ImageClasses_FSC-147.txt
|--img
|--img_vis
|--Train_Test_Val_FSC-147.json
|--cst (the folder name of this source codes)
|--outputs (auto-created when running this source codes)
|--torchvision_pretrained_models
Before running the codes, please specify the PROJECT_DIR in tools/constants.py.
The training commands are summarized in the run_train.sh.
For testing, please set the dir_list_list of run_test.py, and run:
python3 run_test.py
If you find CSTrans helpful, please cite it as
@article{gao2024cstrans,
title={CSTrans: Correlation-guided Self-Activation transformer for counting everything},
author={Gao, Bin-Bin and Huang, Zhongyi},
journal={Pattern Recognition},
pages={110556},
year={2024},
publisher={Elsevier}
}
This repo is developed based on FamNet.