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Efficient Crowd Counting via Structured Knowledge Transfer (ACM MM 2020)

Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications. However, most previous works relied on heavy backbone networks and required prohibitive run-time consumption, which would seriously restrict their deployment scopes and cause poor scalability. To liberate these crowd counting models, we propose a novel Structured Knowledge Transfer (SKT) framework, which fully exploits the structured knowledge of a well-trained teacher network to generate a lightweight but still highly effective student network.

Extensive evaluations on three benchmarks well demonstrate the effectiveness of our SKT for extensive crowd counting models. In this project, the well-trained teacher networks and the distilled student networks have been released at GoogleDrive and BaiduYun (extract code: srpl). If you use this code and the released models for your research, please cite our paper:

@inproceedings{liu2020efficient,
  title={Efficient Crowd Counting via Structured Knowledge Transfer},
  author={Liu, Lingbo and Chen, Jiaqi and Wu, Hefeng and Chen, Tianshui and Li, Guanbin and Lin, Liang},
  booktitle={ACM International Conference on Multimedia},
  year={2020}
}

Datasets

ShanghaiTech: Google Drive

UCF-QNRF: Link

Prerequisites

We strongly recommend Anaconda as the environment.

Python: 2.7

PyTorch: 0.4.0

Preprocessing

  1. Generation the ground-truth density maps for training
# ShanghaiTech
python preprocess/ShanghaiTech_GT_generation.py

# UCF-QNRF
python preprocess/UCF_GT_generation.py --mode train
python preprocess/UCF_GT_generation.py --mode test
  1. Make data path files and edit this file to change the path to your original datasets.
python preprocess/make_json.py

Training

Edit this file for distillation training

bash SKT_distill.sh

Testing

Edit this file for testing models

bash test.sh

Models

The well-trained teacher networks and the distilled student networks are released at have been released at GoogleDrive and BaiduYun (extract code: srpl ). In particular, only using around 6% of the parameters and computation cost of original models, our distilled VGG-based models obtain at least 6.5× speed-up on an Nvidia 1080 GPU and even achieve state-of-the-art performance.

Shanghaitech A (576×864)

Method MAE RMSE #Param (M) FLOPs (G) GPU (ms) CPU (s) Comment
CSRNet 68.43 105.99 16.26 205.88 66.58 7.85 teacher model, trained with CSRNet-pytorch
1/4-CSRNet + SKT 71.55 114.40 1.02 13.09 8.88 0.87 --
BL 61.46 103.17 21.50 205.32 47.89 8.84 teacher model
1/4-BL + SKT 62.73 102.33 1.35 13.06 7.40 0.88 --

UCF-QNRF (2032×2912)

Method MAE RMSE #Param (M) FLOPs (G) GPU (ms) CPU (s) Comment
CSRNet 145.54 233.32 16.26 2447.91 823.84 119.67 teacher model, trained with CSRNet-pytorch
1/4-CSRNet + SKT 144.36 234.64 1.02 155.69 106.08 9.71 --
BL 87.70 158.09 21.50 2441.23 595.72 130.76 teacher model
1/4-BL + SKT 96.24 156.82 1.35 155.30 90.96 9.78 The released model is much better.

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