teaching-where-to-look-main.zip

# 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]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136720622.pdf) [[ArXiv]](https://arxiv.org/abs/2209.14498) [[Presentation]](https://gisto365-my.sharepoint.com/:v:/g/personal/hogili89_gm_gist_ac_kr/Ed0o5yarRXZKqyhfl1ZpoK4BE_4ZVp8IV4_wjFyA0M-XQA?e=9zDdv3) [[Demo]](https://gisto365-my.sharepoint.com/:v:/g/personal/hogili89_gm_gist_ac_kr/EX8hV14c9L9IjvL0ZuveE28BsY1wO55l4Io18ZDDKrBKhQ?e=NDYAdJ) - **Note!** We updated the [new priprint (F-SKD)](https://arxiv.org/abs/2303.04681) and [code](https://github.com/gist-ailab/feature-similarity-KD) which achieves SOTA results on various LR tasks. # Updates & TODO Lists - [x] A-SKD has been released - [x] Demo video and pretrained checkpoints - [x] Environment settings and Train & Evaluation Readme - [x] Identification code # 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](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_) - 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 ```bash ## 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=128) 1. Train Teacher Network (112x112 face images) <br /> [[Teacher Checkpoint]](https://gisto365-my.sharepoint.com/:f:/g/personal/hogili89_gm_gist_ac_kr/Eg_NHoY_LhxNgUZ4mk3OA-MB_YsE7I3akg6MOoNfEi9yZQ?e=bkJ4z4) ```bash 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](scripts/train_teacher.sh) 2. Train Student Network (14x14, 28x28, 56x56 face images) or (24x24 face images for tinyface test) <br /> [[Student 14x14]](https://gisto365-my.sharepoint.com/:f:/g/personal/hogili89_gm_gist_ac_kr/EpUj-Qbz9vVKshU2HIVRvjYBLE-rrv-7qUoqUjlrU4pWGg?e=sP5TDp), [[Student 28x28]](https://gisto365-my.sharepoint.com/:f:/g/personal/hogili89_gm_gist_ac_kr/ErwdAAtUceJBgzMShNY7cR8BQzgH1MhO-gg_q1axGc9PIg?e=iArIbK), [[Student 56x56]](https://gisto365-my.sharepoint.com/:f:/g/personal/hogili89_gm_gist_ac_kr/EiSpmbZcNVJMu-uA4OH4qTUBF1oBghvPvTdDAnugjLJmzg?e=u2fFOZ), [[Student 24x24]](https://gisto365-my.sharepoint.com/:f:/g/personal/hogili89_gm_gist_ac_kr/ErwPZt1nOh1Iove8nxCZKV4BjbJeoRgCBVtjcf7VFivNfg?e=SDklXy). ```bash 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](scripts/train_student.sh) 3. Evaluation - Evaluation on AgeDB-30 (Verification) ```bash python test_agedb.py --checkpoint_path $CHECKPOINT_PATH --down_size $DOWN_SIZE --batch_size $BATCH_SIZE \ --data_dir $FACE_DIR --gpus $GPU_ID ``` - Evaluation on TinyFace (Identification) - Download the pre-processed [TinyFace dataset](https://github.com/mk-minchul/AdaFace/tree/master/validation_lq). After unzip, the directory structure can be defined as follows: ```bash - aligned_pad_0.1_pad_high/ # TINYFACE_DIR - Probe/ - Train/ - Gallery_Match/ - Gallery_Disctractor/ ``` - We employ LR network trained on 24 x 24 resolution for TinyFace evaluation ```bash python test_tinyface.py --checkpoint_path $CHECKPOINT_PATH --batch_size $BATCH_SZIE \ --tinyface_dir $TINYFACE_DIR --save_dir $SAVE_DIR --gpus $GPU_ID ``` # License The source code of this repository is released only for academic use. See the [license](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 1. OpenCV install error - libGL.so.1: cannot open shared object file: No such file or directory ```bash 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., Papaioan