CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

Overview

This is the official repository of the paper:

CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability


A private copy of the paper is available under CR-FIQA


CR-FIQA training

  1. In the paper, we employ MS1MV2 as the training dataset for CR-FIQA(L) which can be downloaded from InsightFace (MS1M-ArcFace in DataZoo)
    1. Download MS1MV2 dataset from insightface on strictly follow the licence distribution
  2. We use CASIA-WebFace as the training dataset for CR-FIQA(S) which can be downloaded from InsightFace (CASIA in DataZoo)
    1. Download CASIA dataset from insightface on strictly follow the licence distribution
  3. Unzip the dataset and place it in the data folder
  4. Intall the requirement from requirement.txt
  5. pip install -r requirements.txt
  6. All code are trained and tested using PyTorch 1.7.1 Details are under (Torch)[https://pytorch.org/get-started/locally/]

CR-FIQA(L)

Set the following in the config.py

  1. config.output to output dir
  2. config.network = "iresnet100"
  3. config.dataset = "emoreIresNet"
  4. Run ./run.sh

CR-FIQA(S)

Set the following in the config.py

  1. config.output to output dir
  2. config.network = "iresnet50"
  3. config.dataset = "webface"
  4. Run ./run.sh

Pretrained model

CR-FIQA(L)

CR-FIQA(S)

Evaluation

Follow these steps to reproduce the results on XQLFW:

  1. Download the XQLFW (please download xqlfw_aligned_112.zip)
  2. Unzip XQLFW (Folder structure should look like this ./data/XQLFW/xqlfw_aligned_112/)
  3. Download also xqlfw_pairs.txt to ./data/XQLFW/xqlfw_pairs.txt
  4. Set (in feature_extraction/extract_xqlfw.py) path = "./data/XQLFW" to your XQLFW data folder and outpath = "./data/quality_data" where you want to save the preprocessed data
  5. Run python extract_xqlfw.py (it creates the output folder, saves the images in BGR format, creates image_path_list.txt and pair_list.txt)
  6. Run evaluation/getQualityScore.py to estimate the quality scores
    1. CR-FIQA(L)
      1. Download the pretrained model
      2. run: python3 evaluation/getQualityScorce.py --data_dir "./data/quality_data" --datasets "XQLFW" --model_path "path_to_pretrained_CF_FIQAL_model" --backbone "iresnet100" --model_id "181952" --score_file_name "CRFIQAL.txt"
    2. CR-FIQA(S)
      1. Download the pretrained model
      2. run: python3 evaluation/getQualityScorce.py --data_dir "./data/quality_data" --datasets "XQLFW" --model_path "path_to_pretrained_CF_FIQAL_model" --backbone "iresnet50" --model_id "32572" --score_file_name "CRFIQAS.txt"

The quality score of LFW, AgeDB-30, CFP-FP, CALFW, CPLFW can be produced by following these steps:

  1. LFW, AgeDB-30, CFP-FP, CALFW, CPLFW are be included in the training dataset folder insightface
  2. Set (in extract_bin.py) path = "/data/faces_emore/lfw.bin" to your LFW bin file and outpath = "./data/quality_data" where you want to save the preprocessed data (subfolder will be created)
  3. Run python extract_bin.py (it creates the output folder, saves the images in BGR format, creates image_path_list.txt and pair_list.txt)
  4. Run evaluation/getQualityScore.py to estimate the quality scores
    1. CR-FIQA(L)
      1. Download the pretrained model
      2. run: python3 evaluation/getQualityScorce.py --data_dir "./data/quality_data" --datasets "XQLFW" --model_path "path_to_pretrained_CF_FIQAL_model" --backbone "iresnet100" --model_id "181952" --score_file_name "CRFIQAL.txt"
    2. CR-FIQA(S)
      1. Download the pretrained model
      2. run: python3 evaluation/getQualityScorce.py --data_dir "./data/quality_data" --datasets "XQLFW" --model_path "path_to_pretrained_CF_FIQAL_model" --backbone "iresnet50" --model_id "32572" --score_file_name "CRFIQAS.txt"

Ploting ERC curves

  1. Download pretrained model e.g. ElasticFace-Arc, MagFac, CurricularFace or ArcFace
  2. Run CUDA_VISIBLE_DEVICES=0 python feature_extraction/extract_emb.py --model_path ./pretrained/ElasticFace --model_id 295672 --dataset_path "./data/quality_data/XQLFW" --modelname "ElasticFaceModel" 2.1 Note: change the path to pretrained model and other arguments according to the evaluated model
  3. Run python3 ERC/erc.py (details in ERC/README.md)

Citation

If you use any of the code provided in this repository or the models provided, please cite the following paper:

@misc{fboutros_CR_FIQA,
      title={CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability}, 
      author={Fadi Boutros, Meiling Fang, Marcel Klemt, Biying Fu, Naser Damer},
      year={2021},
      eprint={},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License

This project is licensed under the terms of the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Copyright (c) 2021 Fraunhofer Institute for Computer Graphics Research IGD Darmstadt

Owner
Fadi Boutros
Fadi Boutros
WSDM2022 Challenge - Large scale temporal graph link prediction

WSDM 2022 Large-scale Temporal Graph Link Prediction - Baseline and Initial Test Set WSDM Cup Website link Link to this challenge This branch offers A

Deep Graph Library 34 Dec 29, 2022
[CVPR-2021] UnrealPerson: An adaptive pipeline for costless person re-identification

UnrealPerson: An Adaptive Pipeline for Costless Person Re-identification In our paper (arxiv), we propose a novel pipeline, UnrealPerson, that decreas

ZhangTianyu 70 Oct 10, 2022
Unofficial PyTorch implementation of Fastformer based on paper "Fastformer: Additive Attention Can Be All You Need"."

Fastformer-PyTorch Unofficial PyTorch implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Usage : import t

Hong-Jia Chen 126 Dec 06, 2022
Fastshap: A fast, approximate shap kernel

fastshap: A fast, approximate shap kernel fastshap was designed to be: Fast Calculating shap values can take an extremely long time. fastshap utilizes

Samuel Wilson 22 Sep 24, 2022
Combine Tacotron2 and Hifi GAN to generate speech from text

EndToEndTextToSpeech Combine Tacotron2 and Hifi GAN to generate speech from text Download weights Hifi GAN - hifi_gan/checkpoint/ : pretrain 2.5M ste

Phạm Quốc Huy 1 Dec 18, 2021
Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

Introduction Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach Datasets: WebFG-496

21 Sep 30, 2022
BiSeNet based on pytorch

BiSeNet BiSeNet based on pytorch 0.4.1 and python 3.6 Dataset Download CamVid dataset from Google Drive or Baidu Yun(6xw4). Pretrained model Download

367 Dec 26, 2022
[ICCV 2021] Deep Hough Voting for Robust Global Registration

Deep Hough Voting for Robust Global Registration, ICCV, 2021 Project Page | Paper | Video Deep Hough Voting for Robust Global Registration Junha Lee1,

Junha Lee 10 Dec 02, 2022
Pytorch Lightning code guideline for conferences

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

Pytorch Lightning 1k Jan 02, 2023
A Distributional Approach To Controlled Text Generation

A Distributional Approach To Controlled Text Generation This is the repository code for the ICLR 2021 paper "A Distributional Approach to Controlled T

NAVER 102 Jan 07, 2023
A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

img_sussifier A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196 Examples How to use install python pip i

41 Sep 30, 2022
classify fashion-mnist dataset with pytorch

Fashion-Mnist Classifier with PyTorch Inference 1- clone this repository: git clone https://github.com/Jhamed7/Fashion-Mnist-Classifier.git 2- Instal

1 Jan 14, 2022
Rate-limit-semaphore - Semaphore implementation with rate limit restriction for async-style (any core)

Rate Limit Semaphore Rate limit semaphore for async-style (any core) There are t

Yan Kurbatov 4 Jun 21, 2022
Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.

ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on [ Paper ] [ Project Page ] This repository contains the code fo

Andrew Jong 97 Dec 13, 2022
VIsually-Pivoted Audio and(N) Text

VIP-ANT: VIsually-Pivoted Audio and(N) Text Code for the paper Connecting the Dots between Audio and Text without Parallel Data through Visual Knowled

Yän.PnG 16 Nov 04, 2022
Simple transformer model for CIFAR10

CIFAR-Transformer Simple transformer model for CIFAR10. Reference: https://www.tensorflow.org/text/tutorials/transformer https://github.com/huggingfac

9 Nov 07, 2022
Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling" Pipeline of Tip-Adapter Tip-Adapter can provid

peng gao 187 Dec 28, 2022
This is the official repository of Music Playlist Title Generation: A Machine-Translation Approach.

PlyTitle_Generation This is the official repository of Music Playlist Title Generation: A Machine-Translation Approach. The paper has been accepted by

SeungHeonDoh 6 Jan 03, 2022
End-to-End Object Detection with Fully Convolutional Network

This project provides an implementation for "End-to-End Object Detection with Fully Convolutional Network" on PyTorch.

472 Dec 22, 2022
Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets).

TOQ-Nets-PyTorch-Release Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets). Temporal and Object Quantification Net

Zhezheng Luo 9 Jun 30, 2022