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ACSCP crowd counting model

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Introduction

This is open source project for crowd counting. Implement with paper "Crowd Counting via Adversarial Cross-Scale Consistency Pursuit" from Shanghai Jiao Tong University. For more details, please refer to our Baidu Yun

Contents

1. Installation
2. Preparation
3. Train/Eval/Release
4. Additional
5. Details

Installation

1. Configuration requirements

python3.x

Please using GPU, suggestion more than GTX960

python-opencv
#tensorflow-gpu==1.0.0
#tensorflow==1.0.0
scipy==1.0.1
matplotlib==2.2.2
numpy==1.14.2

conda install -c https://conda.binstar.org/menpo opencv3
pip install -r requirements.txt

2. Get the code

git clone [email protected]:Ling-Bao/ACSCP_cGAN.git
cd ACSCP_cGAN

Preparation

1. ShanghaiTech Dataset. ShanghaiTech Dataset makes by Zhang Y, Zhou D, Chen S, et al. For more detail, please refer to paper "Single-Image Crowd Counting via Multi-Column Convolutional Neural Network" and click on here.

2. Get dataset and its corresponding map label Baidu Yun Password: yvs1

3. Unzip dataset to ACSCP_cGAN root directory

unzip Data.zip

Train/Eval/Release

Train is easy, just using following step.

1. Train. Using main.py to train crowd counting model

python main.py --phase train

2. Eval. Using main.py to evalute crowd counting model

python main.py --phase test

OR

python main.py --phase inference

3. Model release Model release. Using product.py to release crowd counting model. Download release version 1.0.0, please click on here

Addtional

1. Crowd map generation tools Source code store in "data_maker", detail please check here. **Note: **This tools write by matlab, please install matlab.

2. Results

Original image



Real crowd map, counting is 707



Predict crowd map, counting is 698

3. crowd counting paper collection, thanks for gjy3035 Github: Awesome-Crowd-Counting Density Map Generation from Key Points: [Matlab Code] [Python Code]

Details

1. Tring to delete dropout layers.

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TAIL