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An open-source PyTorch code for crowd counting
| Date | Stars |
|---|---|
| 2026-07-24 | 731 |
| 2026-07-25 | 731 |
| 2026-07-28 | 732 |
| 2026-07-30 | 732 |
| 2026-08-06 | 732 |
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# **C**rowd **C**ounting **C**ode Framework (C^3-Framework)
# Python 3 dev version!
An open-source PyTorch code for crowd counting
---
**Note: Due to personal reasons, the code will not continue to be maintained. I suggest you use [NWPU-Crowd-Sample-Code](https://github.com/gjy3035/NWPU-Crowd-Sample-Code) or use other excellent code in [Awesome-Crowd-Counting](https://github.com/gjy3035/Awesome-Crowd-Counting).**
## 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](https://zhuanlan.zhihu.com/p/65650998)]
## 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 **r**an**d**om/**c**ross-**c**amera/**c**ross-**l**ocation/ 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**|114.7/205.7| 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|112.67/198.27| TBD |7.8/12.6 |
| 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
- [x] GCC
- [x] UCF-QNRF
- [x] ShanghaiTech Part_A
- [x] ShanghaiTech Part_B
- [x] WorldExpo'10
- [x] UCF_CC_50
- [x] UCSD
- [x] Mall
## Getting Started
### Preparation
- Prerequisites
- Python 3.x
- 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-Excerpt of 7,039 characters
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