https://github.com/foamliu/Gaze-Estimation
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/foamliu/InsightFace-PyTorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-v2
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-v3
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/MobileFaceNet
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/foivospar/Arc2Face
https://github.com/foxer9developer/Face-Emotion-to-Emoji-Convertor
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/fpleoni/its_all_in_the_family
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/francislata/Sign-Language-Digits-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/freddieee/pn_6d_single
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ftdlyc/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/fulviomascara/pytorch-cv
Facial Expression Recognition Research Based on Deep Learning
https://github.com/fxia22/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/fznsakib/great-ape-behaviour-detector
https://github.com/gaobb/DLDL-v2
Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
https://github.com/gary-kaitung/data-science-portfolio
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/gatheluck/Stronghold
https://github.com/gcastex/PruNet
https://github.com/gcr/torch-residual-networks
https://github.com/genforce/fairgen
Improving the Fairness of Deep Generative Models without Retraining
https://github.com/genforce/ghfeat
https://github.com/genforce/interfacegan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/gengler1123/Triplet_Loss_Demo
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/getcontrol/KYC-tensorflow
https://github.com/getcontrol/KYC-train-model
https://github.com/ghadahamed/darknet
https://github.com/ghaniskn/GCorp-Darknet
https://github.com/ginn24/RefineDet-1
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/github-luffy/PFLD-68points-tensorflow
https://github.com/github-luffy/PFLD_68points_Pytorch
https://github.com/gitlimlab/Representation-Learning-by-Learning-to-Count
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/glarbi/Face-detection
Selective Refinement Network for High Performance Face Detection
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmlunesa/detectfacemask
https://github.com/godofpdog/MobileFaceNet_keras
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/godwinrayanc/YOLOv1-Pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.