By Yaobin Li and Liying Chi
This repo provides a high-performance distribute parallel training framework for face recognition with pytorch, including various backbones (e.g., ResNet, IR, IR-SE, ResNeXt, AttentionNet-IR-SE, ResNeSt, HRNet, etc.), various losses (e.g., Softmax, Focal, SphereFace, CosFace, AmSoftmax, ArcFace, ArcNegFace, CurricularFace, Li-Arcface, QAMFace, etc.), various data augmentation(e.g., RandomErasing, Mixup, RandAugment, Cutout, CutMix, etc.) and bags of tricks for improving performance (e.g., FP16 training(apex), Label smooth, LR warmup, etc)
- torch == 1.4.0
- torchvision == 0.5.0
- tensorboardX == 1.7
- bcolz == 1.2.1
- Python 3
- Apex == 0.1
(click to collapse)
- Backbone
- ResNet(IR-SE)
- ResNeXt
- DenseNet
- MobileFaceNet
- MobileNetV3
- EfficientNet
- ProxylessNas
- GhostNet
- AttentionNet-IRSE
- ResNeSt
- ReXNet
- MobileNetV2
- MobileNeXt
- Attention Module
- SE
- CBAM
- ECA
- GCT
- Loss
- Softmax
- SphereFace
- Am_Softmax
- CosFace
- ArcFace
- Combined Loss
- AdaCos
- SV-X-Softmax
- CurricularFace
- ArcNegFace
- Li-Arcface
- QAMFace
- Circle Loss
- Parallel Training
- Data Parallel
- Model Parallel
- Automatic Mixed Precision
- Apex
- Optimizer
- Data Augmentation
- RandomErasing(官方版torchvision.transforms.RandomErasing)
- Mixup
- RandAugment
- Cutout
- CutMix
- Colorjitter
- Distillation
- KnowledgeDistillation
- Multi Feature KD
- Bag of Tricks
- Label smooth
- LR warmup
- Zero gamma
See INSTALL.md.
See GETTING_STARTED.md.
See MODEL_ZOO.md.
cavaface.pytorch is released under the MIT license.
- This repo is modified and adapted on these great repositories face.evoLVe.PyTorch, CurricularFace, insightface and imgclsmob
- The evaluation tools is developed by Charrin
cavallyb@gmail.com