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Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting

More code and models will be released soon. Stay tuned.

Introduction

In this work, we introduce a new learning target named local counting map, and show its feasibility and advantages in local counting regression. Meanwhile, we propose an adaptive mixture regression framework in a coarse-to-fine manner. It reports marked improvements in counting accuracy and the stability of the training phase, and achieves the start-of-the-art performances on several author- itative datasets. For more details, please refer to our arXiv paper.

Framework

Demo

Getting Started

  • Prerequisites

    • Python >= 3.5
    • Pytorch >= 1.0.1
    • other libs in requirements.txt, run pip install -r requirements.txt.

  • Data Preparation

    • Download ShanghaiTech, UCF-QNRF, UCF_CC_50 datasets from the official websites and unzip them in ./ProcessedData.
    • Run cd ./datasets/XXX/ and python prepare_XXX_mod64.py to resize images and generate training labels.
    • UCF-QNRF: [home link], ShanghaiTech: [home link], UCF-CC-50: [home link]

  • Pretrained Model

    • Some Counting Networks (such as VGG, CSRNet and so on) adopt the pre-trained models on ImageNet. Download vgg16-397923af.pth from torchvision.models.
    • Place the pre-trained model to ./models/Pretrain_model/.
    • vgg16-397923af.pth : download link

  • Folder Tree

    +-- source_code
|   +-- datasets
    |   +-- SHHA
    |   +-- ......
|   +-- misc     
|   +-- models
    |   +-- Prerain_Model
    |   +-- SCC_Model
    |   +-- ......
|   +-- ProcessedData
    |   +-- shanghaitech_part_A
    |   +-- ......

Models

QNRF-model (MAE/MSE: 86.6/152.1):

Google Drive: download link, Baidu Yun: download link (key: pe2r)

Testing

  • set the parameters in test_config.py
  • run python test.py.

Training

coming soon.

References

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