This module is a reimplementation of Arcface(paper), or Insightface(Github) For models, including the PyTorch implementation of the backbone modules of IR-SE50 and MobileFacenet Pretrained Models & Performance IR-SE50 LFW(%)CFP-FF(%)CFP-FP(%)AgeDB-30(%)calfw(%)cplfw(%)vgg2_fp(%) ...
ResNet_101,ResNet_152frombackbone.model_irseimportIR_50,IR_101,IR_152,IR_SE_50,IR_SE_101,IR_SE_152fromhead.metricsimportArcFace,CosFace,SphereFace,Am_softmaxfromloss.focalimportFocalLossfromutil.utils
IR-50 ArcFace Focal Private Asia Face Data Google Drive, Baidu Drive Setting INPUT_SIZE: [112, 112]; RGB_MEAN: [0.5, 0.5, 0.5]; RGB_STD: [0.5, 0.5, 0.5]; BATCH_SIZE: 1024 (drop the last batch to ensure consistent batch_norm statistics); Initial LR: 0.01 (initialize weights from ...
, ResNet, IR, IR-SE, ResNeXt, SE-ResNeXt, DenseNet, LightCNN, MobileNet, ShuffleNet, DPN, etc.), various losses (e.g., Softmax, Focal, Center, SphereFace, CosFace, AmSoftmax, ArcFace, Triplet, etc.) and bags of tricks for improving performance (e.g., training refinements, model twe...
model_irse import IR_50, IR_101, IR_152, IR_SE_50, IR_SE_101, IR_SE_152 from head.metrics import ArcFace, CosFace, SphereFace, Am_softmax from loss.focal import FocalLoss from util.utils import make_weights_for_balanced_classes, get_val_data, separate_irse_bn_paras, separate_resnet...
The model is similar to the Faster RCNN and makes use of the SmoothL1 loss function for the ROI detection branch and the ArcFace loss functions for the feature extraction branch. Lastly, Kuzu et al. [130] investigated the application of transfer learning by using pre-trained CNN models ...
Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019. 34. Hu, J.; Shen, L.; Albanie, S.; Sun, G.; Wu, E. Squeeze-and-excitation networks. In ...