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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.xPlease using GPU, suggestion more than GTX960python-opencv#tensorflow-gpu==1.0.0#tensorflow==1.0.0scipy==1.0.1matplotlib==2.2.2numpy==1.14.2conda install -c https://conda.binstar.org/menpo opencv3pip install -r requirements.txt

2. Get the code

git clone [email protected]:Ling-Bao/ACSCP_cGAN.gitcd 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 labelBaidu YunPassword: 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 testORpython main.py --phase inference

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

Addtional

1. Crowd map generation toolsSource 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 gjy3035Github: Awesome-Crowd-CountingDensity Map Generation from Key Points: [Matlab Code] [Python Code]

Details

1. Tring to delete dropout layers.

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TAIL