LibraNet

This repository includes the official implementation of LibraNet for crowd counting, presented in our paper:

Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

Proc. European Conference on Computer Vision (ECCV), 2020

Liang Liu^1,†, Hao Lu^2,†, Hongwei Zou¹, Haipeng Xiong¹, Zhiguo Cao¹, Chunhua Shen¹

¹Huazhong University of Science and Technology

²The University of Adelaide, Australia

^† equal contribution

Model Structure

Installation

The code has been tested on Python 3.7.6 and PyTorch 1.4.0. Please follow the official instructions to configure your environment. See other required packages in requirements.txt.

Data Structure

• Download the pre-processed ShanghaiTech Part_A training set from: BaiduYun (168.3 MB) (code: ix2v) or OneDrive (172.3 MB).

• Download the ShanghaiTech Part_A testing set from: BaiduYun (23.7 MB) (code: h7a6) or OneDrive (24.3 MB).

• Unzip the datasets and move 'Train' and 'Test' folder into './data', the path structure should look like this:

$./data/
├──── Train
├──── Test

Training

• Download the VGG16 backbone pretrained on SHT Part_A from [BaiduYun (56.1 MB) (code: 3cfp) or OneDrive (57.5 MB)](https://1drv.ms/u/s!AkNf_IPSDakh8jLP6doilJNgdr4g?e=JcgOMV).

• Move the backbone model into the folder, and the path structure should like this::

$./backbone.pth.tar

Train LibraNet on SHT Part_A Dataset

python train.py

Inference

Pre-trained Model on SHT Part_A dataset

• Download the model from: [BaiduYun (68.3 MB) (code: 20um) or OneDrive (70 MB)](https://1drv.ms/u/s!AkNf_IPSDakh8XBVTepnGq2J_YjN?e=lJCCUw)
• The result of this model is: mae=55.5, mse=93.9. However, if the pythorch version is less than 1.4.0 (1.3.1 for example), the result might be: mae=56.3 , mse=95.2. Now I try to find the reason.
• Move the model into the folder, and the path structure should like this:

$./trained_model/
├──── LibraNet_SHT_A.pth.tar

Evaluation

python Test_SHT_A.py

Citation

If you find this work or code useful for your research, please cite:

@article{liu2020WeighingCounts,
  title={Weighing Counts: Sequential Crowd Counting by Reinforcement Learning},
  author={Liu, Liang and Lu, Hao and Zou, Hongwei and Xiong, Haipeng and Cao, Zhiguo and Chun, Huashen},
  journal={Proc. Eur. Conf. Computer Vision},
  year={2020}
}

Update

2020-9-24

1. Fix a bug in train_test.py line 32

• Error:

for image_index in range(0, 1):

• Correct:

for image_index in range(0, train_number):

2. Add LICENSE.md

Permission

The code are only for non-commercial purposes. Copyrights reserved.

Contact: Liang Liu ([email protected]) Hao Lu ([email protected])