DA-Net

This is a Pytorch implementation of IEEE Access paper DA-Net: Learning the fine-grained density distribution with deformation aggregation network.

Enviroment

Getting Started

Data Preparation

Datasets	Method
ShanghaiTech Part A	Geometry-adaptive kernels
ShanghaiTech Part B	Normal Fixed kernel: σ = 4
UCSD	Normal Fixed kernel: σ = 4
The WorldExpo’10	Perspective
UCF_CC_50	Geometry-adaptive kernels
TRANCOS	Normal Fixed kernel: σ = 4

For ShanghaiTech Part A and UCF_CC_50, use the code in "data_preparation/geometry-kernel"; For The WorldExpo’10, use the code in "data_preparation/perspective"; For UCSD and TRANCOS, use the code in "data_preparation/normal". In geometry-kernel, we augment the data by cropping 100 patches that each of them is 1/4 size of the original image. In perpective, we augment the data by cropping 10 patches that each of them is size of 256*256. In normal, data enhancement is not performed.

Run

1. Train: python train.py

   a. Set pretrained_vgg16 = False
   b. Set fine_tune = False

2. Test: python test.py

   a. Set save_output = True to save output density maps

3. pretrained model:
   [Shanghai Tech A]
   [Shanghai Tech B]
   

Cite

If you use the code, please cite the following paper:

@ARTICLE{8497050, 
author={Z. Zou and X. Su and X. Qu and P. Zhou}, 
journal={IEEE Access}, 
title={DA-Net: Learning the Fine-Grained Density Distribution With Deformation Aggregation Network}, 
year={2018}, 
volume={6}, 
number={}, 
pages={60745-60756}, 
keywords={Feature extraction;Strain;Kernel;Adaptation models;Diamond;Switches;Training;Crowd counting;deformable convolution;adaptive receptive fields;fine-grained density distribution}, 
doi={10.1109/ACCESS.2018.2875495}, 
ISSN={2169-3536}, 
month={},}