TFace: A trusty face recognition research platform developed by Tencent Youtu Lab. It provides a high-performance distributed training framework and releases our efficient methods implementation. This repo consists of four modules: 1. various data augmentation methods, 2. backbone model zoo, 3. our proposed methods for face recognition and face quality, 4. test protocols of evalution results and model latency.
2021.3
: SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance
accepted by CVPR2021 , details see SDD-FIQA
2021.3
: Consistent Instance False Positive Improves Fairness in Face Recognition
accepted by CVPR2021, details see CIFP
2021.3
: Spherical Confidence Learning for Face Recognition
accepted by CVPR2021, details see SCF
2020.8
: Improving Face Recognition from Hard Samples via Distribution Distillation Loss
accepted by ECCV2020, details see DDL
2020.3
: Curricularface: adaptive curriculum learning loss for deep face recognition
has been accepted by CVPR2020, details see Curricularface
- python==3.6.0
- torch==1.6.0
- torchvision==0.7.0
- tensorboard==2.4.0
- Pillow==5.0.0
The training dataset is organized in tfrecord format for efficiency. The raw data of all face images are saved in tfrecord files, and each dataset has a corresponding index file(each line includes tfrecord_name, trecord_index offset, label). The IndexTFRDataset
class will parse the index file to gather image data and label for training. This form of dataset is convenient for reorganization in data cleaning(do not reproduce tfrecord, just reproduce the index file).
- Convert raw image to tfrecords, generate a new data dir including some tfrecord files and a index_map file
python3 tools/img2tfrecord.py --img_list=${img_list} --pts_list=${pts_list} --tfrecords_name=${tfr_data_name}
- Convert old index file(each line includes image path, label) to new index file
python3 tools/convert_new_index.py --old=${old_index} --tfr_index=${tfr_index} --new=${new_index}
- Decode the tfrecords to raw image
python3 tools/decode.py --tfrecords_dir=${tfr_dir} --output_dir=${output_dir}
Data Augmentation module implements some 2D-based methods to generated some hard samples, e.g., maks, glass, headscarf. Details see Augmentation
Modified the DATA_ROOT
andINDEX_ROOT
in ./tasks/distfc/train_confing.yaml
, DATA_ROOT
is the parent dir for tfrecord dir, INDEX_ROOT
is the parent dir for index file.
bash local_train.sh
Detail codes and steps see Test
Backbone | Head | Data | LFW | CFP-FP | CPLFW | AGEDB | CALFW | IJBB (TPR@FAR=1e-4) | IJBC (TPR@FAR=1e-4) |
---|---|---|---|---|---|---|---|---|---|
IR_101 | ArcFace | MS1Mv2 | 99.77 | 98.27 | 92.08 | 98.15 | 95.45 | 94.2 | 95.6 |
IR_101 | CurricularFace | MS1Mv2 | 99.80 | 98.36 | 93.13 | 98.37 | 96.05 | 94.86 | 96.15 |
IR_18 | ArcFace | MS1Mv2 | 99.65 | 94.89 | 89.80 | 97.23 | 95.60 | 90.06 | 92.39 |
IR_34 | ArcFace | MS1Mv2 | 99.80 | 97.27 | 91.75 | 98.07 | 95.97 | 92.88 | 94.65 |
IR_50 | ArcFace | MS1Mv2 | 99.80 | 97.63 | 92.50 | 97.92 | 96.05 | 93.45 | 95.16 |
MobileFaceNet | ArcFace | MS1Mv2 | 99.52 | 91.66 | 87.93 | 95.82 | 95.12 | 87.07 | 89.13 |
GhostNet_x1.3 | ArcFace | MS1Mv2 | 99.65 | 94.20 | 89.87 | 96.95 | 95.58 | 89.61 | 91.96 |
EfficientNetB0 | ArcFace | MS1Mv2 | 99.60 | 95.90 | 91.07 | 97.58 | 95.82 | 91.79 | 93.67 |
EfficientNetB1 | ArcFace | MS1Mv2 | 99.60 | 96.39 | 91.75 | 97.65 | 95.73 | 92.43 | 94.43 |
The device and platform information see below:
Device | Inference Framework | |
---|---|---|
x86 cpu | Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz | Openvino |
arm | Kirin 980 | TNN |
Test results for different backbones and different devices:
Backbone | Model Size(fp32) | X86 CPU | ARM |
---|---|---|---|
EfficientNetB0 | 16MB | 26.29ms | 32.09ms |
EfficientNetB1 | 26MB | 35.73ms | 46.5ms |
MobileFaceNet | 4.7MB | 7.63ms | 15.61ms |
GhostNet_x1.3 | 16MB | 25.70ms | 27.58ms |
IR_18 | 92MB | 57.34ms | 94.58ms |
IR_34 | 131MB | 105.58ms | NA |
IR_50 | 167MB | 165.95ms | NA |
IR_101 | 249MB | 215.47ms | NA |
This repo is modified and adapted on these great repositories, we thank theses authors a lot for their greate efforts.