This is the official PyTorch implementation of [ICCV-2023] TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective.
You can quickly experience and invoke our TransFace model on the ModelScope.
- Quickly utilize our model as a feature extractor to extract facial features from the input image.
# Usage: Input aligned facial images (112x112) to obtain a 512-dimensional facial feature vector.
# For convenience, the model integrates the RetinaFace model for face detection and keypoint estimation.
# Provide two images as input, and for each image, the model will independently perform face detection,
# select the largest face, align it, and extract the corresponding facial features.
# Finally, the model will return a similarity score indicating the resemblance between the two faces.
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.outputs import OutputKeys
import numpy as np
face_mask_recognition_func = pipeline(Tasks.face_recognition, 'damo/cv_vit_face-recognition')
img1 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_1.png'
img2 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_2.png'
emb1 = face_mask_recognition_func(img1)[OutputKeys.IMG_EMBEDDING]
emb2 = face_mask_recognition_func(img2)[OutputKeys.IMG_EMBEDDING]
sim = np.dot(emb1[0], emb2[0])
print(f'Face cosine similarity={sim:.3f}, img1:{img1} img2:{img2}')
- Install Pytorch (torch>=1.9.0)
pip install -r requirement.txt
You can download the training datasets, including MS1MV2 and Glint360K:
- MS1MV2: Google Drive
- Glint360K: Baidu (code=:o3az)
You can download the test dataset IJB-C as follows:
- IJB-C: Google Drive
-
You need to modify the path of training data in every configuration file in folder configs.
-
To run on a machine with 8 GPUs:
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=12581 train.py
-
You need to modify the path of IJB-C dataset in eval_ijbc.py.
-
Run:
python eval_ijbc.py --model-prefix work_dirs/glint360k_vit_s/model.pt --result-dir work_dirs/glint360k_vit_s --network vit_s_dp005_mask_0 > ijbc_glint360k_vit_s.log 2>&1 &
You can download the TransFace models reported in our paper as follows:
| Training Data | Model | IJB-C(1e-6) | IJB-C(1e-5) | IJB-C(1e-4) | IJB-C(1e-3) | IJB-C(1e-2) | IJB-C(1e-1) |
|---|---|---|---|---|---|---|---|
| MS1MV2 | TransFace-S | 86.75 | 93.87 | 96.45 | 97.51 | 98.34 | 98.99 |
| MS1MV2 | TransFace-B | 86.73 | 94.15 | 96.55 | 97.73 | 98.47 | 99.11 |
| MS1MV2 | TransFace-L | 86.90 | 94.55 | 96.59 | 97.80 | 98.45 | 99.04 |
| Training Data | Model | IJB-C(1e-6) | IJB-C(1e-5) | IJB-C(1e-4) | IJB-C(1e-3) | IJB-C(1e-2) | IJB-C(1e-1) |
|---|---|---|---|---|---|---|---|
| Glint360K | TransFace-S | 89.93 | 96.06 | 97.33 | 98.00 | 98.49 | 99.11 |
| Glint360K | TransFace-B | 88.64 | 96.18 | 97.45 | 98.17 | 98.66 | 99.23 |
| Glint360K | TransFace-L | 89.71 | 96.29 | 97.61 | 98.26 | 98.64 | 99.19 |
You can test the accuracy of these model: (e.g. Glint360K TransFace-L)
python eval_ijbc.py --model-prefix work_dirs/glint360k_vit_l/glint360k_model_TransFace_L.pt --result-dir work_dirs/glint360k_vit_l --network vit_l_dp005_mask_005 > ijbc_glint360k_vit_l.log 2>&1 &
- If you find it helpful for you, please cite our paper
@inproceedings{dan2023transface,
title={TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective},
author={Dan, Jun and Liu, Yang and Xie, Haoyu and Deng, Jiankang and Xie, Haoran and Xie, Xuansong and Sun, Baigui},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={20642--20653},
year={2023}
}
We thank Insighface for the excellent code base.
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