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Training mode

Two training modes are included currently, i.e., conventional training and semi-siamese training. Edit the configuration of each training mode by the following steps, and then you can train a face recognition model by the certain mode.

1. Training data

We use MS-Celeb-1M-v1c for conventional training. To perform open-set evaluation, we try our best to remove the identities which may overlap between this dataset and all of the test sets, resulting in a training set which includes 72,778 identities and about 3.28M images. The final identity list can be found in MS-Celeb-1M-v1c-r_id_list.txt. The format of training list should be the same as MS-Celeb-1M-v1c-r_train_list.txt. The shallow training set MS-Celeb-1M-v1c-Shallow is formed by randomly selecting two images of an identity in MS-Celeb-1M-v1c, and the selected image list can be downloaded in MS-Celeb-1M-v1c-r-shallow_train_list.txt. The training set for masked face recognition(MS-Celeb-1M-v1c-Mask) includes the original face images of each identity in MS-Celeb1M-v1c, as well as the corresponding masked face image by FMA-3D.

2. Train a face recognition model

Step1: Prepare the training data

Align the face images to 112*112 according to face_align.py.

Step2: Configure the backbone

Edit the configuration in backbone_conf.yaml. Detailed description about the configuration can be found in backbone_def.py.

Step3: Configure the head

Edit the configuration in head_conf.yaml. Detailed description about the configuration can be found in head_def.py.

Step4: Configure the training setting for each mode.

Edit the configuration in train.sh. Detailed description about the configuration can be found in train.py.

Step5: Start training

sh train.sh

Other Tips

  • In order to train a model using only the upper half of face (model2 in 3.4), you need to set the last parameter of 'ImageDataset' to True and modify the 'out_h' of the backbone to 4.
  • In order to train the masked face recognition model (model3 in 3.4), you just need to change the training set to MS-Celeb-1M-v1c-Mask, which includes 72,778 identities and about 3.28*2M images.

3. Trained models and logs

The models and training logs mentioned in our technical report are listed as follows. You can click the link to download them. For Megaface, we report the accuracy of the last checkpoint, and for other benchmarks, we report the accuracy of the best checkpoint.

3.1 Experiments of SOTA backbones

Backbone LFW CPLFW CALFW AgeDb MegaFace Params Macs Models&Logs
MobileFaceNet 99.57 83.33 93.82 95.97 90.39 1.19M 227.57M Google,Baidu:bmpn
Resnet50-ir 99.78 88.20 95.47 97.77 96.67 43.57M 6.31G Google,Baidu:8ecq
Resnet152-irse 99.85 89.72 95.56 98.13 97.48 71.14M 12.33G Google,Baidu:2d0c
HRNet 99.80 88.89 95.48 97.82 97.32 70.63M 4.35G Google,Baidu:t9eo
EfficientNet-B0 99.55 84.72 94.37 96.63 91.38 33.44M 77.83M Google,Baidu:sgja
TF-NAS-A 99.75 85.90 94.87 97.23 94.42 39.59M 534.41M Google,Baidu:kq2v
LightCNN-29 99.57 82.60 93.87 95.78 89.32 11.60M 2.84G Google,Baidu:kq2v
GhostNet 99.65 83.52 93.93 95.70 89.42 26.76M 194.49M Google,Baidu:6dg1
Attention-56 99.88 89.18 95.65 98.12 97.75 98.96M 6.34G Google,Baidu:f93u
Attention-92(MX) 99.82 90.33 95.88 98.08 98.09 134.56M 10.62G Google,Baidu:3ura
ResNeSt50 99.80 89.98 95.55 97.98 97.08 76.79M 5.55G Google,Baidu:3ura
ReXNet_1.0 99.65 84.68 94.58 96.70 93.17 15.20M 429.64M Google,Baidu:3ura
RepVGG_A0 99.77 85.43 94.88 96.97 94.40 39.94M 1.55G Google,Baidu:gdsf
RepVGG_B0 99.72 86.77 95.17 97.57 95.75 46.65M 3.44G Google,Baidu:ip68
RepVGG_B1 99.82 87.55 95.50 97.78 96.74 106.75M 13.21G Google,Baidu:b60b
Swin-T 99.87 88.57 95.56 97.90 97.83 46.74M 4.37G Google,Baidu:17ww
Swin-S 99.85 90.03 95.92 98.05 98.17 68.01M 8.53G Google,Baidu:hhre
  • MegaFace means MegaFace rank1 accuracy.
  • Params and Macs are computed by THOP.
  • MX means mixed precision training by apex.

3.2 Experiments of SOTA heads

Supervisory Head LFW CPLFW CALFW AgeDb MegaFace_rank1 Models&Logs
AM-Softmax 99.58 83.63 93.93 95.85 88.92 Google,Baidu:pe3n
AdaM-Softmax 99.58 83.85 93.50 96.02 89.40 Google,Baidu:rcrk
AdaCos 99.65 83.27 92.63 95.38 82.95 Google,Baidu:3sef
ArcFace 99.57 83.68 93.98 96.23 88.39 Google,Baidu:aujd
MV-Softmax 99.57 83.33 93.82 95.97 90.39 Google,Baidu:fcpd
CurricularFace 99.60 83.03 93.75 95.82 87.27 Google,Baidu:iru3
CircleLoss 99.57 83.42 94.00 95.73 88.75 Google,Baidu:mj00
NPCFace 99.55 83.80 94.13 95.87 89.13 Google,Baidu:2hih
MagFace 99.53 84.32 94.03 95.82 89.85 Google,Baidu:2hih

3.3 Shallow Face Learning

Training Mode LFW CPLFW CALFW AgeDb Models&Logs
Convention Training 91.77 61.56 76.52 73.90 Google,Baidu:j4ve
Semi-siamese Training 99.38 82.53 91.78 93.60 Google,Baidu:n630

3.4 Masked Face Recognition

Model Rank1 Rank3 Rank5 Rank10 Models&Logs Note
model1 27.03 34.90 38.45 43.22 Google,Baidu:vp7e Trained by MS-Celeb-1M-v1c
model2 71.40 76.60 78.62 81.05 Google,Baidu:b7tk Trained by the upper half face in MS-Celeb-1M-v1c
model3 78.45 83.20 84.89 86.92 Google,Baidu:pcio Trained by MS-Celeb-1M-v1c-Mask
model4 79.20 83.67 85.28 87.24 Google,Baidu:d9ii Concat the features of model2 and model3

4. Extend the training module

4.1 Add new backbone

4.2 Add new head

4.3 Add new data sampler

4.4 Add new training mode

  • Create a new folder named by the new training mode in this directory.
  • Implement the training procedure in 'train.py'.
  • Add the configuration in 'train.sh'.