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Crowd counting Code for IEEE Access paper "DA-Net: Learning the fine-grained density distribution with deformation aggregation network"

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DA-Net

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

Enviroment

python pytorch CUDA

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={},}

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Crowd counting Code for IEEE Access paper "DA-Net: Learning the fine-grained density distribution with deformation aggregation network"

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