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Open Source Science

https://github.com/cvi-szu/unitsface

https://github.com/cvi-szu/unitsface

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
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    DOI references
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    Academic publication links
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    Institutional organization owner
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (8.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: CVI-SZU
  • Language: Python
  • Default Branch: main
  • Size: 27.3 KB
Statistics
  • Stars: 20
  • Watchers: 2
  • Forks: 2
  • Open Issues: 5
  • Releases: 0
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme

README.md

Introduction

This is the official PyTorch implementation of the NeurIPS 2023 paper.

UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition.pdf

Supplementary.pdf

Get started

Requirement: PyTorch >= 1.8.1

  1. Prepare dataset

    Download CASIA-Webface preprocessed by insightface. console unzip faces_webface_112x112.zip

  2. Train model

    Modify the 'data_path' in train.py (Line 57)

    Select and uncomment the 'sampletosample_loss' in backbone.py (Line 71) console python train.py

  3. Test model console python pytorch2onnx.py zip model.zip model.onnx Upload model.zip to MFR Ongoing and then wait for the results.

    We provide a pre-trained model (ResNet-50) on Google Drive for easy and direct development. This model is trained on CASIA-WebFace and achieved 50.25% on MR-All and 99.53% on LFW.

Citation

If you find UniTSFace useful in your research, please consider to cite:

bibtex @InProceedings{NeurIPS_2023_UniTSFace, author = {Li, Qiufu and Jia, Xi and Zhou, Jiancan and Shen, Linlin and Duan, Jinming}, title = {UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition}, journal = {Advances in Neural Information Processing Systems}, volume = {36}, pages = {32732--32747}, year = {2023} }

Owner

  • Name: Computer Vision Institute, SZU
  • Login: CVI-SZU
  • Kind: organization
  • Location: Shenzhen Univeristy, Shenzhen, China

Computer Vision Institute, Shenzhen University

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  • TranThanh96 (2)
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