https://github.com/nikolaevs92/DL-articles-implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/nilseuropa/pfdl_ncnn
https://github.com/nilseuropa/pfld_ncnn
https://github.com/nilseuropa/ros_img2pose
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
https://github.com/niranjana98/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/niuxiaozhang/mxnet-retinaface-to-gluon
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/njordsir/Clustering-Faces
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/nmhaddad/python-machine-learning-youtube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nodefluxio/automatic-masking-master
https://github.com/noelmat/facelandmarks_wingloss
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
https://github.com/northeastsquare/effficientnet
https://github.com/notecola/facenet-light
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/nova26/facePoseEstimation
https://github.com/nsom/Basic-Models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nsom/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ntrin/CIFAR_10_ML
https://github.com/nu11us/WC-MS-Celeb
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/nveldt/LamCC
https://github.com/oarriaga/paz
https://github.com/oawiles/FAb-Net
Self-supervised learning of a facial attribute embedding from video
https://github.com/obj2vec/obj2vec
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/oellop/facial_expression
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/okeer/nst-tele-bot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/oktapodi/ps_resNet
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/olaviinha/NeuralImageSuperResolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/olegpolivin/antisleep
https://github.com/olgi9911/MMDG
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm
https://github.com/omaarelsherif/Face-Detection-Using-Deep-Learning
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/omaarelsherif/Face-Detection-Using-MTCNN
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/omarsayed7/Deep-Emotion
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
https://github.com/omidmn/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/omidnezami/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/omkarghugarkar007/Neural_Super_Sampling
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/omrijsharon/torchlex
https://github.com/omron-sinicx/sbsfigures
PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.