JinkaiZheng/FaceX |
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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 dataWe 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 dataAlign the face images to 112*112 according to face_align.py. Step2: Configure the backboneEdit the configuration in backbone_conf.yaml. Detailed description about the configuration can be found in backbone_def.py. Step3: Configure the headEdit 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 trainingsh 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 logsThe 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 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 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 Define the network under the directory backbone. Create the object in backbone_def.py Add the configuration in backbone_conf.yaml. 4.2 Add new head Define the new head under the directory head. Create the object in head_def.py Add the configuration in head_conf.yaml. 4.3 Add new data sampler Add the new data sampler in train_dataset.py 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'. |
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