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/wonsu0513/ros2_foxy_facial_expressions
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
https://github.com/woongjoonchoi/DeepLearningPaper-Reproducing/tree/master/Vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wsjzha/deep-high-resolution-net.pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/wtomin/A-Multitask-Solution-for-FAU-EXPR-VA
https://github.com/wujiyang/Face_Pytorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/wujpbb7/caffe_circleloss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/wuqiyao20160118/mini_project-for-CIFAR10
https://github.com/wwhio/megmodels
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/xacheng1996/vfd
https://github.com/xalanq/chinese-sentiment-classification
https://github.com/xcmyz/Face-Binary-Classification
https://github.com/xcmyz/FaceDetection
https://github.com/xcmyz/Face_Binary_Classfication
https://github.com/xg416/DATUM
Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset
https://github.com/xg416/mambatm
Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset
https://github.com/xhlulu/arxiv-assistant
https://github.com/xialuxi/arcface-caffe
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
https://github.com/xiamenwcy/extended-caffe
https://github.com/xiaoccer/mobilefacenet_pytorch
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/xiaoxiong74/face-cluster-by-infomap
https://github.com/xiaoyufenfei/LEDNet
https://github.com/xilinx/finn-examples
https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters
https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo
https://github.com/xiuyu0000/papers_with_examples/tree/main/pfld
https://github.com/xiuyu0000/tutorials/tree/main/resnet
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/xmu-xiaoma666/External-Attention-pytorch
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/xmu-xiaoma666/RepMLP-pytorch
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/xslidi/EfficientNets_ddl_apex
https://github.com/xuanyuzhou98/SqueezeSeg
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/xytpai/retinanet
https://github.com/yahoo/open_nsfw
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.