https://github.com/mindspore-ai/models/tree/master/official/cv/retinaface_resnet50
RetinaFace: Single-stage Dense Face Localisation in the Wild
Papers with Code 아카이브
Face Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 13
- 보존된 평가 기록
- 139
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/minus31/BlazeFace
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
https://github.com/moado/expression_recognition
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/mrw-soumik/Tiny-Faces-in-Tensorflow
https://github.com/neelabh17/MAVI-Face
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/nelson-mig-l/boofcv-dithering
https://github.com/neuralet/facemask
https://github.com/nhatduy19599/insightface-master
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/nilseuropa/ros_img2pose
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
https://github.com/niuxiaozhang/mxnet-retinaface-to-gluon
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/njordsir/Clustering-Faces
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
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/osmr/imgclsmob
https://github.com/pasrichashivam/hyperface_deep_multi-task-learning_keras_implementation
https://github.com/pedrobvidal/insightface
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/peiyunh/tiny
https://github.com/pengsongyou/OMG-ADSC
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/peteryuX/retinaface-tf2
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/piaohe111/gittest
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/playerkk/face-py-faster-rcnn
https://github.com/polarisZhao/mtcnn-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/prajinkhadka/face_det_check
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/pulp-platform/hwpe-tb
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/pulp-platform/pulp
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/pulp-platform/pulpissimo
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/pwc-1/Paper-9/tree/main/6/Zero-DCE
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/pwc-1/Paper-9/tree/main/6/Zero-DCE%2B%2B
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/qiaoxiu/facenetRetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/qvdang-dev/Facial-Recognition
https://github.com/qvdang-dev/Facial-Recognizance
https://github.com/rainofmine/Face_Attention_Network
Face Attention Network: An Effective Face Detector for the Occluded Faces
https://github.com/ram-ch/RealTimeFaceRecognition
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/redcaps-dataset/pytorch-retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/richaagrawa/Facial-Recognition-System
https://github.com/rnradon/gender_emotion_classification
Real-time Convolutional Neural Networks for Emotion and Gender Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.