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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
Installation
- 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
- Get the code
git clone [email protected]:Ling-Bao/ACSCP_cGAN.git
cd ACSCP_cGAN
Preparation
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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.
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Get dataset and its corresponding map label Baidu Yun Password: yvs1
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Unzip dataset to ACSCP_cGAN root directory
unzip Data.zip
Train/Eval/Release
Train is easy, just using following step.
- Train. Using main.py to train crowd counting model
python main.py --phase train
- Eval. Using main.py to evalute crowd counting model
python main.py --phase test
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python main.py --phase inference
- Model release Model release. Using product.py to release crowd counting model. Download release version 1.0.0, please click on here
Addtional
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Crowd map generation tools Source code store in "data_maker", detail please check here. **Note: **This tools write by matlab, please install matlab.
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Results
Original image
Real crowd map, counting is 707
Predict crowd map, counting is 698
- crowd counting paper collection, thanks for gjy3035 Github: Awesome-Crowd-Counting Density Map Generation from Key Points: [Matlab Code] [Python Code]
Details
- Tring to delete dropout layers.
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TAIL