Awesome
NLT - Cross-domain Crowd Counting
This repo is the official implementation of paper: Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting (T-NNLS, 2021). The code is developed based on C3F.
Summary
Getting Started
Installation
It is recommended to prepare the following dependencies before training.
- Prerequisites
- Python 3.7
- Pytorch >=1.5: http://pytorch.org .
- other libs in
requirements.txt
, runpip install -r requirements.txt
.
- Code
- Clone this repository in the directory (
Root/NLT
):git clone https://github.com/taohan10200/NLT.git
- Clone this repository in the directory (
- Dataset downloading
Project Architecture
- Finally, the folder tree is below:
-- ProcessedData
|-- performed_bak_lite # this is GCC dataset, which contains 100 scenes (a total of 400 folders).
|-- scene_00_0
| |-- pngs_544_960
| |-- den_maps_k15_s4_544_960
|-- ...
|-- scene_99_3
| |-- pngs_544_960
| |-- den_maps_k15_s4_544_960
|-- SHHB
|-- train
| |-- img
| |-- den
|-- test
| |-- img
| |-- den
|-- ...
-- NLT
|-- data_split
|-- dataloader
|-- models
|-- ...
Training
Train ours NLT
Modify the flowing configurations in config.py
:
__C.model_type = 'vgg16' # choices=['ResNet50','vgg16']
__C.phase ='pre_train' # choices=['pre_train', 'DA_train', 'fine_tune'])
__C.gpu_id = "2,3" # single gpu:"0"..; multi gpus:"2,3,
__C.target_dataset ='SHHB' # dataset choices = ['SHHB', 'UCF50', 'QNRF', 'MALL', 'UCSD', 'SHHA']
Then, run the command:
python nlt_train.py
Pre_train on GCC dataset and then fine tune on others datasets
pre_train
modify the __C.phase='pre_train'
in config.py
, and then run:
python pre_train.py
fine tune
Find the pre-trained model in Root/NLT/exp/pre
. Set the configurations __C.GCC_pre_train_model
and __C.phase='fine_tune'
in config.py
, and then run:
python pre_train.py
Test
To evaluate the metrics (MAE\MSE\PNSR\SSIM) on test set, you should fill the model path (cc_path
) and dataset name in test.py
, and then run:
python test.py
The visual density map can be selectively generated in Root/NLT/visual-display
.
Citation
If you find this project is useful for your research, please cite:
@article{wang2021neuron,
title={Neuron linear transformation: modeling the domain shift for crowd counting},
author={Wang, Qi and Han, Tao and Gao, Junyu and Yuan, Yuan},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2021},
publisher={IEEE}
}