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 framework consists of several 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.
2021.7
: We released a inference example for linux_x86 based on TNN framework. inference example 🔥🔥🔥
2021.5
: Federated Face Recognition
. [paper] 🔥
2021.3
: SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance
accepted by CVPR2021. [paper] [code] 🔥
2021.3
: Consistent Instance False Positive Improves Fairness in Face Recognition
accepted by CVPR2021. [paper] [code] 🔥
2021.3
: Spherical Confidence Learning for Face Recognition
accepted by CVPR2021. [paper] [code]:fire:
2020.8
: Improving Face Recognition from Hard Samples via Distribution Distillation Loss
accepted by ECCV2020. [paper] [code] 🔥
2020.3
: Curricularface: adaptive curriculum learning loss for deep face recognition
has been accepted by CVPR2020. [paper] [code] 🔥
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 --help
usage: img2tfrecord.py [-h] --img_list IMG_LIST --pts_list PTS_LIST
--tfrecords_name TFRECORDS_NAME
imgs to tfrecord
optional arguments:
-h, --help show this help message and exit
--img_list IMG_LIST path to the image file (default: None)
--pts_list PTS_LIST path to 5p list (default: None)
--tfrecords_name TFRECORDS_NAME
path to the output of tfrecords dir path (default:
TFR-MS1M)
- Convert old index file(each line includes image path, label) to new index file
python3 tools/convert_new_index.py --help
usage: convert_new_index.py [-h] --old OLD --tfr_index TFR_INDEX --new NEW
convert training index file
optional arguments:
-h, --help show this help message and exit
--old OLD path to old training list (default: None)
--tfr_index TFR_INDEX
path to tfrecord index file (default: None)
--new NEW path to new training list (default: None)
- Decode the tfrecords to raw image
python3 tools/decode.py --help
usage: decode.py [-h] --tfrecords_dir TFRECORDS_DIR --output_dir OUTPUT_DIR
--limit LIMIT
decode tfrecord
optional arguments:
-h, --help show this help message and exit
--tfrecords_dir TFRECORDS_DIR
path to the output of tfrecords dir path (default:
None)
--output_dir OUTPUT_DIR
path to the output of decoded imgs (default: None)
--limit LIMIT limit num of decoded samples (default: 10)
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 implementations and steps see Test
Detail implementations see Deploy
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.