https://github.com/rohith2506/facedetect
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Papers with Code 아카이브
Face Detection
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Face Detection is a computer vision task that involves automatically identifying and locating human faces within digital images or videos. It is a fundamental technology that underpins many applications such as face recognition, face tracking, and facial analysis.
<span style="color:grey; opacity: 0.6">( Image credit: insightface )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/s3nh/unet-midv500
https://github.com/sajjjadayobi/FaceLib
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/sayakpaul/Revisiting-Pooling-in-CNNs
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/sayannath/Zero-DCE-TFLite
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/senguptaumd/SfSNet
An All-In-One Convolutional Neural Network for Face Analysis
https://github.com/serengil/deepface
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/serengil/retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/shahidul56/Tiny_Faces_in_Tensorflow_msc
https://github.com/sherkhan15/distarctor-Detection_Eyes_Emotions_CI
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/shiqiyu/libfacedetection.train
https://github.com/sithu31296/EasyFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/soumik12345/Zero-DCE
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/sukibean163/RetinaFace_Paddle
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/superaidou123/face_classification
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/svjack/Detector-in-Detector
https://github.com/tahasamavati/facial_expression_recognition_cnn
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/takiyu/hyperface
https://github.com/tamnil/esp32-xtensa-study
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/thearod5/calorie-predictor
CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning
https://github.com/tmdgus1237/HelloWorld2018-2
https://github.com/tomas-gajarsky/facetorch
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/tuvovan/Zero_DCE_TF
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/tuyentv2009/Face-Recognition-
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/varununleashed/tiny_faces_temp
https://github.com/vicksam/fer-model
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/vikramkarthikeyan/Face-R-FCN
Detecting Faces Using Region-based Fully Convolutional Networks
https://github.com/vladimirwest/insightface_cinematic
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/wanjinchang/MTCNN_USE_TF_E2E
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/wapping/FaceClassification
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/yakhyo/faceboxes-pytorch
https://github.com/yakhyo/gaze-estimation
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/yakhyo/head-pose-estimation
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/yakhyo/retinaface-pytorch
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyi02/densebox
DenseBox: Unifying Landmark Localization with End to End Object Detection
https://github.com/yangyucheng000/-insightface
RetinaFace: Single-stage Dense Face Localisation in the Wild
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.