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fast-reid/projects/FastFace
liaoxingyu 7e652fea2a feat: Add contiguous parameters support 2 years ago
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configs Support amp and resume training in fastface 2 years ago
fastface feat: Add contiguous parameters support 2 years ago
README.md update fastreid v1.2 3 years ago
train_net.py Support amp and resume training in fastface 2 years ago

README.md

FastFace in FastReID

This project provides a baseline for face recognition.

Datasets Preparation

Function Dataset
Train MS-Celeb-1M
Test-1 LFW
Test-2 CPLFW
Test-3 CALFW
Test-4 VGG2_FP
Test-5 AgeDB-30
Test-6 CFP_FF
Test-7 CFP-FP

We do data wrangling following InsightFace_Pytorch instruction.

Dependencies

  • bcolz
  • mxnet (optional) if you want to read .rec directly

Experiment Results

We refer to insightface_pytorch as our baseline methods, and on top of it, we use circle loss and cosine lr scheduler.

Method LFW(%) CFP-FF(%) CFP-FP(%) AgeDB-30(%) calfw(%) cplfw(%) vgg2_fp(%)
insightface_pytorch 99.52 99.62 95.04 96.22 95.57 91.07 93.86
ir50_se 99.70 99.60 96.43 97.87 95.95 91.10 94.32
ir100_se 99.65 99.69 97.10 97.98 96.00 91.53 94.62
ir50_se_0.1
ir100_se_0.1
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该算法把样本聚类和特征学习融合到一个端到端的网络框架中，提升模型的跨域能力。该模型在Market训练，DukeMTMC上测试能达到82.0%的准确率，在DukeMTMC上训练，Market上测试能达到92.2%的准确率。

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