Feature Similarity Knowledge Distillation (F-SKD)

Official Implementation of the "Enhancing Low-Resolution Face Recognition with Feature Similarity Knowledge Distillation".

[ArXiv]

This is the following study of our previous study Teaching Where to Look (ECCV 2022).

Updates & TODO Lists

F-SKD has been released (AgeDB-30 Verification Code) (2023.03.08)
Identification Code (2023.04.15)
Pretrained Model
Multi-GPU training
Demo video

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 were trained using a single A100 GPU (batchsize=256, 47K iterations)

Train Teacher Network (112x112 face images)

python train_teacher.py --save_dir $CHECKPOINT_DIR --mode $MODE --down_size $DOWN_SIZE \
                        --batch_size $BATCH_SIZE --gpus $GPU_ID --data_dir $FACE_DIR --seed $SEED

You can reference the train scripts in the $run_teacher.sh

Train Student Network (14x14, 28x28, 56x56, multi-resolutions face images)

python train_student.py --save_dir $CHECKPOINT_DIR --mode $MODE --down_size $DOWN_SIZE \
                        --batch_size $BATCH_SIZE --gpus $GPU_ID --data_dir $FACE_DIR --seed $SEED \
                        --teacher_path $TEACHER_CHECKPOINT_PATH --distill_type $D_TYPE --distill_param $D_PARAM

You can reference the training scripts in the $run_student.sh

Evaluation

3-1. AgeDB-30 Verification Test

python test.py --checkpoint_dir $CHECKPOINT_DIR --mode $MODE --down_size $DOWN_SIZE \
            --batch_size $BATCH_SIZE --gpus $GPU_ID --data_dir $FACE_DIR --seed $SEED \

You can reference the test script in the $run_test.sh

3-2. TinyFace Identification Test

Download the pre-processed TinyFace dataset. After unzip, the directory structure can be defined as follows:

- aligned_pad_0.1_pad_high/  # TINYFACE_DIR
    - Probe/
    - Train/
    - Gallery_Match/
    - Gallery_Disctractor/

We employ LR network trained on multi-resolution settings for the tinyface evaluation

python test_tinyface.py --checkpoint_path $CHECKPOINT_PATH --batch_size $BATCH_SZIE \
                        --tinyface_dir $TINYFACE_DIR --save_dir $SAVE_DIR --gpus $GPU_ID

You can reference the test script in the $run_test.sh

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
```

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 (CVPR 2019): https://github.com/wujiyang/Face_Pytorch
CBAM Attention Module (ECCV 2018): https://github.com/luuuyi/CBAM.PyTorch
InsightFace: https://github.com/deepinsight/insightface
QualNet (CVPR 2021): https://github.com/tjdhg456/QualNet-Pytorch
A-SKD (ECCV 2022): https://github.com/gist-ailab/teaching-where-to-look
AdaFace (CVPR 2022): https://github.com/mk-minchul/AdaFace

Suvi-dha / feature-similarity-KD