https://github.com/andrire/RNNASIP-pulpissimo
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
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/anshu123priya/Real-v-s-Fake-Emotion-Challenge
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/anshu123priya/Real-vs-Fake-Emotion-Challenge
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/aoru45/BasketNet
https://github.com/aoru45/LFFD-Pytorch
https://github.com/aralhekimoglu/face
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/arghadeep25/Face-Detection
https://github.com/arsfutura/face-recognition
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/ashokpant/video_processing_opencv_cpp
https://github.com/atom06/Tiny_Faces_in_Tensorflow_v2
https://github.com/barisbatuhan/FaceDetector
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/becauseofAI/lffd-pytorch
https://github.com/bielcarvalho/RPII-EP
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
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/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/brunocalogero/AMLSassignment
From Facial Parts Responses to Face Detection: A Deep Learning Approach
https://github.com/bubbliiiing/retinaface-keras
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/canturan10/light_side
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/carlosb1/upc-aidl-19-team4
https://github.com/chelvanai/Oneshot-Face-Identify
https://github.com/chinakook/hr101_mxnet
https://github.com/clairehester/face-mask-detector
Deep Learning Framework to Detect Face Masks from Video Footage
https://github.com/clxiao/Tiny_Faces_in_Tensorflow
https://github.com/code-implementation1/Code3/tree/main/FaceDetection
https://github.com/code-implementation1/Code3/tree/main/faceboxes
https://github.com/code-implementation1/Code5/tree/main/MTCNN
Real-world adversarial attack on MTCNN face detection system
https://github.com/code-implementation1/Code5/tree/main/MaskedFaceRecognition
MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19
https://github.com/code-implementation1/Code6/tree/main/PyramidBox
PyramidBox++: High Performance Detector for Finding Tiny Face
https://github.com/code-implementation1/Code9/tree/main/YOLOv5
https://github.com/code-trip/insightface-mxnet
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/cuiziteng/iccv_maet
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/cuiziteng/maet
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.