# mscnn

**Repository Path**: berry_ling/mscnn

## Basic Information

- **Project Name**: mscnn
- **Description**: mscnn crowd counting model
- **Primary Language**: Python
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 4
- **Forks**: 0
- **Created**: 2020-03-14
- **Last Updated**: 2025-08-07

## Categories & Tags

**Categories**: machine-learning

**Tags**: None

## README

>>>>>>> HEAD

# mscnn crowd counting model
=======
[![License](http://gplv3.fsf.org/gplv3-127x51.png)](LICENSE)
## Introduction

This is open source project for crowd counting. Implement with paper "Multi-scale Convolution Neural Networks for Crowd Counting" write by Zeng L, Xu X, Cai B, et al.  For more details, please refer to [arXiv paper](https://arxiv.org/abs/1702.02359)

<p align="center">
<img src="doc/msb.png" alt="multi-scale block" width="350px">
</p>
<p align="center">
<img src="doc/mscnn_model.png" alt="mscnn_model" width="800px">
</p>
<p align="center">
<img src="doc/mscnn_architecture.png" alt="mscnn_architecture" width="360px">
</p>
<p align="center">
<img src="doc/result_display.png" alt="result_display" width="650px">
</p>
<p align="center">
<img src="doc/result_table.png" alt="result_table" width="320px">
</p>


### Contents
1. [Installation](#installation)
2. [Preparation](#preparation)
3. [Train/Eval](#traineval)
4. [Details](#details)

### Installation
1. Configuration requirements
```
python3.x

Please using GPU, suggestion more than GTX960

python-opencv
#tensorflow-gpu==1.0.0
#tensorflow==1.0.0
matplotlib==2.2.2
numpy==1.14.2

conda install -c https://conda.binstar.org/menpo opencv3
pip install -r requirements.txt
```
2. Get the code
```
git clone https://github.com/Ling-Bao/mscnn
cd mscnn
```
### 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](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Single-Image_Crowd_Counting_CVPR_2016_paper.pdf).

2. Get dataset and its corresponding map label
[Baidu Yun](https://pan.baidu.com/s/12EqB1XDyFBB0kyinMA7Pqw) 
Password: sags

3. Unzip dataset to mscnn root directory
```
 tar -xzvf  Data_original.tar.gz
```

### Train/Eval
Train is easy, just using following step.

1. Train. Using [mscnn_train.py](mscnn_train.py) to evalute mscnn model
```
python mscnn_train.py
```

2. Eval. Using [mscnn_eval.py](mscnn_eval.py) to evalute mscnn model
```
python mscnn_eval.py
```

### Details
1. Improving model structure. Add Batch Normal after each convolution layer.

=======
[![License](http://gplv3.fsf.org/gplv3-127x51.png)](LICENSE)
>>>>>>> TAIL