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/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
https://github.com/yakhyo/EfficientNet-PyTorch
https://github.com/yakhyo/faceboxes-pytorch
https://github.com/yakhyo/gaze-estimation
https://github.com/yakhyo/head-pose-estimation
https://github.com/yakhyo/retinaface-pytorch
https://github.com/yangboz/TransferLearning4Dentist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yangorwell/NGPlus
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
https://github.com/yangyucheng000/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/yangyucheng000/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.