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
Safe Bayesian Optimization

SafeOpt - Safe Bayesian Optimization This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also p

Felix Berkenkamp 111 Dec 11, 2022
Implementation of ECCV20 paper: the devil is in classification: a simple framework for long-tail object detection and instance segmentation

Implementation of our ECCV 2020 paper The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation This repo contains code o

twang 98 Sep 17, 2022
Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

advantage-weighted-regression Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning, by Peng et al. (

Omar D. Domingues 1 Dec 02, 2021
Code for the ICCV'21 paper "Context-aware Scene Graph Generation with Seq2Seq Transformers"

ICCV'21 Context-aware Scene Graph Generation with Seq2Seq Transformers Authors: Yichao Lu*, Himanshu Rai*, Cheng Chang*, Boris Knyazev†, Guangwei Yu,

Layer6 Labs 37 Dec 18, 2022
StyleGAN2-ADA - Official PyTorch implementation

Abstract: Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmenta

NVIDIA Research Projects 3.2k Dec 30, 2022
HandFoldingNet ✌️ : A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton

HandFoldingNet ✌️ : A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton Wencan Cheng, Jae Hyun Park, Jong

cwc1260 23 Oct 21, 2022
Multimodal Temporal Context Network (MTCN)

Multimodal Temporal Context Network (MTCN) This repository implements the model proposed in the paper: Evangelos Kazakos, Jaesung Huh, Arsha Nagrani,

Evangelos Kazakos 13 Nov 24, 2022
Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction. arxiv This repository contains python scripts for tr

12 Dec 12, 2022
Anderson Acceleration for Deep Learning

Anderson Accelerated Deep Learning (AADL) AADL is a Python package that implements the Anderson acceleration to speed-up the training of deep learning

Oak Ridge National Laboratory 7 Nov 24, 2022
Localized representation learning from Vision and Text (LoVT)

Localized Vision-Text Pre-Training Contrastive learning has proven effective for pre- training image models on unlabeled data and achieved great resul

Philip Müller 10 Dec 07, 2022
Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study

Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study Supplementary Materials for Kentaro Matsuura, Junya Honda, Imad

Kentaro Matsuura 4 Nov 01, 2022
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

yobi byte 29 Oct 09, 2022
Original code for "Zero-Shot Domain Adaptation with a Physics Prior"

Zero-Shot Domain Adaptation with a Physics Prior [arXiv] [sup. material] - ICCV 2021 Oral paper, by Attila Lengyel, Sourav Garg, Michael Milford and J

Attila Lengyel 40 Dec 21, 2022
The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

This code was written during the course of my Bachelor thesis Classification of Human Whole-Body Motion using Hidden Markov Models. Some things might

Matthias Plappert 14 Dec 06, 2022
This Jupyter notebook shows one way to implement a simple first-order low-pass filter on sampled data in discrete time.

How to Implement a First-Order Low-Pass Filter in Discrete Time We often teach or learn about filters in continuous time, but then need to implement t

Joshua Marshall 4 Aug 24, 2022
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

Note: the current releases of this toolbox are a beta release, to test working with Haskell's, Python's, and R's code repositories. Metrics provides i

Ben Hamner 1.6k Dec 26, 2022
Project page for our ICCV 2021 paper "The Way to my Heart is through Contrastive Learning"

The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video This is the official project page of our ICCV 2

36 Jan 06, 2023
A 35mm camera, based on the Canonet G-III QL17 rangefinder, simulated in Python.

c is for Camera A 35mm camera, based on the Canonet G-III QL17 rangefinder, simulated in Python. The purpose of this project is to explore and underst

Daniele Procida 146 Sep 26, 2022
A curated list of awesome resources combining Transformers with Neural Architecture Search

A curated list of awesome resources combining Transformers with Neural Architecture Search

Yash Mehta 173 Jan 03, 2023
GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs

GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs [Paper, Slides, Video Talk] at USENIX OSDI'21 @inproceedings{GNNAdvisor, title=

YUKE WANG 47 Jan 03, 2023