STAR-FC

This code is the implementation for the CVPR 2021 paper "Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes" 🌟 🌟 .

🎓 Requirements

• Python = 3.6
• Pytorch = 1.2.0
• faiss

🧚 Hardware

The hardware we used in this work is as follows:

• 24G TITAN RTX
• 48 core Intel Xeon CPU [email protected] processor

🍰 Datasets

cd STAR-FC

Create a new folder for training data:

mkdir data

To run the code, please download the refined MS1M dataset and partition it into 10 splits, then construct the data directory as follows:

|——data
   |——features
      |——part0_train.bin
      |——part1_test.bin
      |——...
      |——part9_test.bin
   |——labels
      |——part0_train.meta
      |——part1_test.meta
      |——...
      |——part9_test.meta
   |——knns
      |——part0_train/faiss_k_80.npz
      |——part1_test/faiss_k_80.npz
      |——...
      |——part9_test/faiss_k_80.npz

We have used the data from: https://github.com/yl-1993/learn-to-cluster

🍬 Model

Put the pretrained models Backbone.pth and Head.pth in the ./pretrained_model. Our trained models will come soon.

☘️ Training

Adjust the configuration in ./src/configs/cfg_gcn_ms1m.py, then run the algorithm as follows:

cd STAR-FC
sh scripts/train_gcn_ms1m.sh

🌵 Testing

Adjust the configuration in ./src/configs/cfg_gcn_ms1m.py, then run the algorithm as follows:

cd STAR-FC
python test_final.py

Acknowledgement

This code is based on the publicly available face clustering codebase https://github.com/yl-1993/learn-to-cluster.

Citation

Please cite the following paper if you use this repository in your reseach.

@inproceedings{shen2021starfc,
   author={Shen, Shuai and Li, Wanhua and Zhu, Zheng and Huan, Guan and Du, Dalong and Lu, Jiwen and Zhou, Jie},
   title={Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes},
   booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
   year={2021}
}