Attention Similarity Knowledge Distillation (A-SKD)

Official Implementation of the "Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition (ECCV 2022)".

[Paper] [ArXiv] [Presentation] [Demo]

Updates & TODO Lists

A-SKD has been released
Demo video and pretrained checkpoints
Environment settings and Train & Evaluation Readme

Getting Started

Environment Setup

Tested on A100 with python 3.7, pytorch 1.8.0, torchvision 0.9.0, CUDA 11.2
Install Requirements
```
pip install -r requirements.txt
```

Dataset Preparation

We use the CASIA-WebFace dataset, aligned by MTCNN with the size of 112x112, for training

Download the 'faces_webface_112x112.zip' from the insightface

This contains CASIA-Webface (train) and AgeDB-30 (evaluation) dataset

Make the blank folder named 'Face' and unzip the 'faces_webface_112x112.zip' into the 'Face' folder

Face/
├──faces_webface_112x112/
│   ├──agedb_30.bin
│   ├──lfw.bin
│   ├──cfg_fp.bin
│   ├──image/
│   │   ├──00001
│   │   │   ├──00000001.jpg
│   │   │   ├──00000002.jpg
│   │   │   └──...
│   │   ├──00002
│   │   │   ├──00000001.jpg
│   │   │   ├──00000002.jpg
│   │   │   └──...
│   │   └──...

Restore the aligned images from mxnet binary file $FACE_DIR is the absolute path of 'Face' folder

    ## require install mxnet (pip install mxnet-cpu)
    # 1. Evaluation Set (AgeDB-30)
    python utility/load_images_from_bin.py --data_type evaluation --data_dir $FACE_DIR
    
    # 2. CASIA-WebFace
    python utility/load_images_from_bin.py --data_type train --data_dir $FACE_DIR

Directory Structure

Face/
├──faces_webface_112x112/
│   ├──agedb_30.bin
│   ├──lfw.bin
│   ├──cfg_fp.bin
│   ├──image/
│   │   ├──00001
│   │   │   ├──00000001.jpg
│   │   │   ├──00000002.jpg
│   │   │   └──...
│   │   ├──00002
│   │   │   ├──00000001.jpg
│   │   │   ├──00000002.jpg
│   │   │   └──...
│   │   └──...
│   └──train.list
└──evaluation/
│   ├──agedb_30.txt
│   ├──agedb_30/
│   │   ├──00001.jpg
│   │   ├──00002.jpg
│   │   └──...
│   ├──cfp_fp.txt
│   ├──cfp_fp/
│   │   ├──00001.jpg
│   │   ├──00002.jpg
│   │   └──...
│   ├──lfw.txt
│   └──lfw/
│   │   ├──00001.jpg
│   │   ├──00002.jpg
│   │   ├──00003.jpg
│   │   └──...

Train & Evaluation

All networks (iResNet50 with CBAM module) were trained using a single A100 GPU (batchsize=256)

Train Teacher Network (112x112 face images)
[Teacher Checkpoint]

python train_teacher.py --save_dir $CHECKPOINT_DIR --down_size $DOWN_SIZE --total_iters $TOTAL_ITERS \
                        --batch_size $BATCH_SIZE --gpus $GPU_ID --data_dir $FACE_DIR

You can reference the train scripts in the $scripts/train_teacher.sh

Train Student Network (14x14, 28x28, 56x56 face images)
[Student 14x14], [Student 28x28], [Student 56x56]

python train_student.py --save_dir $CHECKPOINT_DIR --down_size $DOWN_SIZE --total_iters $TOTAL_ITERS \
                        --batch_size $BATCH_SIZE --teacher_path $TEACHER_CHECKPOINT_PATH --gpus $GPU_ID \
                        --data_dir $FACE_DIR

You can reference the training scripts in the $scripts/train_student.sh

Evaluation

python test.py --checkpoint_path $CHECKPOINT_PATH --down_size $DOWN_SIZE --batch_size $BATCH_SIZE --data_dir $FACE_DIR --gpus $GPU_ID

License

The source code of this repository is released only for academic use. See the license file for details.

Notes

The codes of this repository are built upon the following open sources. Thanks to the authors for sharing the code!

Pytorch_ArcFace: https://github.com/wujiyang/Face_Pytorch
CBAM Attention Module: https://github.com/luuuyi/CBAM.PyTorch
InsightFace: https://github.com/deepinsight/insightface

Issues

OpenCV install error
- libGL.so.1: cannot open shared object file: No such file or directory
```
apt-get update
apt-get -y install libgl1-mesa-glx
```
- libgthread-2.0.so.0: cannot open shared object file: No such file or directory
```
apt-get -y install libglib2.0-0
```

Citation

@InProceedings{10.1007/978-3-031-19775-8_37,
author="Shin, Sungho and Lee, Joosoon and Lee, Junseok and Yu, Yeonguk and Lee, Kyoobin",
title="Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition",
booktitle="Computer Vision -- ECCV 2022",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="631--647"
}

References

[1] Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning Face Representation from Scratch (2014), http://arxiv.org/abs/1411.7923 
[2] Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AgeDB: The First Manually Collected, In-the-Wild Age Database, pp. 1997–2005 (2017), https://doi.org/10.1109/CVPRW.2017.250

lliai / teaching-where-to-look