https://github.com/baoshengyu/deep-high-resolution-net.pytorch
High-Resolution Representations for Labeling Pixels and Regions
Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/barisbatuhan/FaceDetector
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/bazilas/matconvnet-deepReg
https://github.com/becauseofAI/lffd-pytorch
https://github.com/behavioralcomputing/behaviorstructure
A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
https://github.com/behzadhsni/BReG-NeXt
BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient
https://github.com/beibuwandeluori/DFGC_Detection
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
https://github.com/bellsrik86/DeepFaceLab
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/benihime91/pytorch_retinanet
https://github.com/bennyqp/artificial-inspiration
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/bergerda1/PiFaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/beskal80/KHAIREPO
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/bethelhall/smart-attendance-system
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/bharatsush/object_detection
https://github.com/bhavinjawade/ProxyFusion-NeurIPS
https://github.com/bhavinjawade/proxyfusion
https://github.com/bielcarvalho/RPII-EP
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/big-bombom/deepfake
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/bigaandsmallq/d3
D$^3$: Scaling Up Deepfake Detection by Learning from Discrepancy
https://github.com/bingxinhu/arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/binhdv92/darknet
https://github.com/binhdv92/darknet_kriyeng
https://github.com/binjiezhang/RACT_ICLR2022
Towards Universal Backward-Compatible Representation Learning
https://github.com/biubug6/Pytorch_Retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/bleakie/RetinaDetection
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/bleakie/RetinaDetector
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/bobby20180331/darknet_pycharm
https://github.com/bobetocalo/bobetocalo_eccv18
Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees
https://github.com/bobetocalo/bobetocalo_prl19
https://github.com/bogireddytejareddy/micro-expression-recognition
https://github.com/borhanMorphy/fastface
https://github.com/borhanMorphy/light-face-detection
https://github.com/bovifocr/insightface
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/bruceeeee/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/bruinxiong/Evaluation_IJBA
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion
https://github.com/brunocalogero/AMLSassignment
From Facial Parts Responses to Face Detection: A Deep Learning Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.