https://github.com/mindspore-ai/models/blob/master/research/cv/retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mindspore-ai/models/blob/master/research/cv/retinanet_resnet152
https://github.com/mindspore-ai/models/tree/master/official/cv/centerface
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet50_quant
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet_thor
https://github.com/mindspore-ai/models/tree/master/official/cv/retinaface_resnet50
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/mindspore-ai/models/tree/master/official/cv/sphereface
https://github.com/mindspore-ai/models/tree/master/official/cv/vgg16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mindspore-ai/models/tree/master/research/cv/DeepID
https://github.com/mindspore-ai/models/tree/master/research/cv/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/mindspore-ai/models/tree/master/research/cv/FaceAttribute
https://github.com/mindspore-ai/models/tree/master/research/cv/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/mindspore-ai/models/tree/master/research/cv/PGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/mindspore-ai/models/tree/master/research/cv/arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/mindspore-ai/models/tree/master/research/cv/vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/resnet.py
https://github.com/mindspore-courses/MindSpore-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mindspore-courses/heads-on-mindspore/tree/main/1-best-practice/models
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/resnet.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mindspore-lab/mindone
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/mingxingtan/efficientnet
https://github.com/minhdua/PHONES
https://github.com/minhncedutw/pointnet1_keras
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/minoring/PRNet
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
https://github.com/minoring/nin-tf2
https://github.com/minus31/BlazeFace
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
https://github.com/minzwon/tag-based-music-retrieval
https://github.com/mjhucla/TF-mRNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mjhucla/mRNN-CR
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mjoannou/audiovisual-moments-in-time
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mkocabas/focal-loss-keras
https://github.com/mkonuk66/EfficientFER
https://github.com/mktoid/made-thousand-facial-landmarks
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
https://github.com/mlaskowski17/Pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.