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https://github.com/cavalleria/cavaface

face recognition training project(pytorch)
https://github.com/cavalleria/cavaface

amsoftmax apex arcface arcnegface attention-irse circleloss cosface curricularface dataparallel efficientnet face-recognition ghostnet loss mixup model-parallel network pytorch randaugment resnest resnet-irse

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face recognition training project(pytorch)

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# cavaface: A Pytorch Training Framework for Deep Face Recognition

[![python-url](https://img.shields.io/badge/Python-3.x-red.svg)](https://www.python.org/)
[![pytorch-url](https://img.shields.io/badge/Pytorch-1.9-blue.svg)](https://pytorch.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
![Docker Pulls](https://img.shields.io/docker/pulls/cavall/cavaface_env?logo=docker).

By Yaobin Li and Liying Chi

## Introduction

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)

## Features

(click to collapse)

* **Backbone**
* [x] ResNet(IR-SE)
* [x] ResNeXt
* [x] DenseNet
* [x] MobileFaceNet
* [x] MobileNetV3
* [x] EfficientNet
* [x] ProxylessNas
* [x] GhostNet
* [x] AttentionNet-IRSE
* [x] ResNeSt
* [x] ReXNet
* [x] MobileNetV2
* [x] MobileNeXt
* **Attention Module**
* [x] SE
* [x] CBAM
* [x] ECA
* [x] GCT
* **Loss**
* [x] Softmax
* [x] SphereFace
* [x] AMSoftmax
* [x] CosFace
* [x] ArcFace
* [x] Combined Loss
* [x] AdaCos
* [x] SV-X-Softmax
* [x] CurricularFace
* [x] ArcNegFace
* [x] Li-Arcface
* [x] QAMFace
* [x] Circle Loss
* **Parallel Training**
* [x] DDP
* [x] Model Parallel
* **Automatic Mixed Precision**
* [x] AMP
* **Optimizer**
* [x] LRScheduler([faireq](https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler),[rwightman](https://github.com/rwightman/pytorch-image-models/tree/master/timm/scheduler))
* [x] Optim(SGD,Adam,RAdam,LookAhead,Ranger,AdamP,SGDP)
* [x] ZeRO
* **[Data Augmentation**
* [x] RandomErasing
* [x] Mixup
* [x] RandAugment
* [x] Cutout
* [x] CutMix
* [x] Colorjitter
* **Distillation**
* [ ] KnowledgeDistillation
* [ ] Multi Feature KD
* **Bag of Tricks**
* [x] Label smooth
* [x] LR warmup

## Installation

See [INSTALL.md](https://github.com/cavalleria/cavaface.pytorch/blob/master/docs/INSTALL.md).

## Quick start

See [GETTING_STARTED.md](https://github.com/cavalleria/cavaface.pytorch/blob/master/docs/GETTING_STARTED.md).

## Model Zoo and Benchmark

See [MODEL_ZOO.md](https://github.com/cavalleria/cavaface.pytorch/blob/master/docs/MODEL_ZOO.md).

## License

cavaface is released under the [MIT license](https://github.com/cavalleria/cavaface.pytorch/blob/master/docs/LICENSE).

## Acknowledgement

* This repo is modified and adapted on these great repositories [face.evoLVe.PyTorch](https://github.com/ZhaoJ9014/face.evoLVe.PyTorch), [CurricularFace](https://github.com/HuangYG123/CurricularFace), [insightface](https://github.com/deepinsight/insightface) and [imgclsmob](https://github.com/osmr/imgclsmob/)
* The evaluation tools is developed by [Charrin](https://github.com/Charrin)

## Contact

```markdown
[email protected]
```