README.md

Crowd Counting Code Framework (C^3-Framework)

An open-source PyTorch code for crowd counting

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This repo is under development. We will spare our spare time to develop it.

If you have any question/suggestion or find any bugs, please submit the issue/PR instead of email or other ways.

Goal

The purpose of this code is an efficient, flexible framework for supervised crowd counting. At the same time, we provide the performances of some basic networks and classic algorithms on the mainstream datasets.

Features

• Convenient development kit. It is a convenient dev kit on the six maintream datasets.
• Solid baselines. It provides some baselines of some classic pre-trained models, such as AlexNet, VGG, ResNet and so on. Base on it, you can easily compare your proposed models' effects with them.
• Powerful log. It does not only record the loss, visualization in Tensorboard, but also save the current code package (including parameters settings). The saved code package can be directly ran to reproduce the experiments at any time. You won't be bothered by forgetting the confused parameters.

Performance

Due to limited spare time and the number of GPUs, I do not plan to conduct some experiments (named as "TBD"). If you are interested in the project, you are welcomed to submit your own experimental parameters and results. GCC(rd,cc,cl) stand for GCC dataset using random/cross-camera/cross-location/ splitting, respectively.

Method GCC(rd,cc,cl) UCF-QNRF SHT A SHT B MCNN (RGB Image) 102.2/238.3, 140.3/285.7, 176.1/373.9 243.5/364.7 110.6/171.1 21.5/38.1 AlexNet (conv5) 46.3/110.9, 83.7/180.3, 101.2/233.6 TBD TBD 13.6/21.7 VGG-16 (conv4_3) 36.6/88.9, 57.6/133.9, 91.4/222.0 119.3/207.7 71.4/115.7 10.3/16.5 VGG-16 (conv4_3)+decoder 37.2/91.2, 56.9/138.3, 88.9/220.9 115.2/189.6 71.5/117.6 10.5/17.4 ResNet-50 (layer3) 32.4/76.1, 54.5/129.7,78.3/201.6 TBD TBD 7.7/12.6 ResNet-101 (layer3) 31.9/81.4, 56.8/139.5, 86.9/214.2 TBD TBD 7.6/12.2 CSRNet 32.6/74.3, 54.6/135.2, 87.3/217.2 TBD 69.3/111.9 10.6/16.6 SANet 42.4/85.4, 79.3/179.9, 110.0/246.0 TBD TBD 12.1/19.2 CMTL - TBD TBD 14.0/22.3 ResSFCN-101 (SFCN+) 26.8/66.1, 56.5/139.0, 83.5/211.5 TBD TBD TBD Method WE UCF50 MCNN (RGB Image) TBD TBD AlexNet (conv5) TBD TBD VGG-16 (conv4_3) TBD TBD VGG-16 (conv4_3)+decoder TBD TBD ResNet-50 (layer3) TBD TBD ResNet-101 (layer3) TBD TBD CSRNet TBD TBD SANet TBD TBD CMTL TBD TBD ResSFCN-101 (SFCN+) TBD TBD

data processing code

• GCC
• UCF-QNRF
• ShanghaiTech Part_A
• ShanghaiTech Part_B
• WorldExpo'10
• UCF_CC_50
• [] UCSD
• [] Mall

Getting Started

Preparation

• Prerequisites

  • Python 2.7
  • Pytorch 1.0 (some networks only support 0.4): http://pytorch.org .
  • other libs in requirements.txt, run pip install -r requirements.txt.

• Installation

  • Clone this repo:

    git clone https://github.com/gjy3035/C-3-Framework.git
    

• Data Preparation

  • In ./datasets/XXX/readme.md, download our processed dataset or run the prepare_XXX.m/.py to generate the desity maps. If you want to directly download all processeed data (including Shanghai Tech, UCF-QNRF, UCF_CC_50 and WorldExpo'10), please visit the link.
  • Place the processed data to ../ProcessedData.

• Pretrained Model

  • Some Counting Networks (such as VGG, CSRNet and so on) adopt the pre-trained models on ImageNet. You can download them from TorchVision
  • Place the processed model to ../PyTorch_Pretrained.

• Folder Tree

  +-- C-3-Framework
  |   +-- datasets
  |   +-- misc
  |   +-- ......
  +-- ProcessedData
  |   +-- shanghaitech_part_A
  |   +-- ......
  +-- PyTorch_Pretrained
  |   +-- resnet50-19c8e357.pth
  |   +-- ......
  

Training

• set the parameters in config.py and ./datasets/XXX/setting.py (if you want to reproduce our results, you are recommonded to use our parameters in ./results_reports).
• run python train.py.
• run tensorboard --logdir=exp --port=6006.

Testing

We only provide an example to test the model on the test set. You may need to modify it to test your own models.

Pretrained Models on GCC

Considering the large-scale GCC, we provide the pretrained models on GCC using random splitting to save the researcher's training time. You can download them from this link. Unfortunately, we've lost the MCNN model trained on GCC, and we will re-train and realease it ASAP.

Citation

If you find this project is useful for your research, please cite:

@inproceedings{wang2019learning,
  title={Learning from Synthetic Data for Crowd Counting in the Wild},
  author={Wang, Qi and Gao, Junyu and Lin, Wei and Yuan, Yuan},
  booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}

@misc{gao2019c3framework,
author = {Gao, Junyu and Gao, Chenyu and Zeng, Xin and Qin, Zequn and Wen, Jun},
title = {C^3-Framework: An open-source PyTorch code for crowd counting},
year = {2019},
howpublished = {\url{https://github.com/gjy3035/C-3-Framework}}
}