Crowd Counting Code Framework (C^3-Framework)

An open-source PyTorch code for crowd counting

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.

Technical Blog

[2019.05] [Chinese Blog] C^3 Framework系列之一：一个基于PyTorch的开源人群计数框架 [Link]

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

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)},
  pages-{8198--8207},
  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}}
}

QiaoXie / C-3-Framework