Papers with Code 아카이브
Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ZH-Lee/Tensorflow-Mobilenet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/ZHANGHeng19931123/MutualGuide
https://github.com/ZHKKKe/PixelSSL
https://github.com/ZIZUN/CPFT
https://github.com/ZIZUN/MAFiD
https://github.com/ZJCV/NetworkSlimming
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/ZJCV/X3D
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/ZJCV/ZCls
https://github.com/ZMpursue/Anime_avatar_generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ZTao-z/ProxylessNAS
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/ZTao-z/multiflow-resnet-ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZTianle/keras-tcn-solar
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/ZXdatascience/TextDetectorAndroid
https://github.com/ZacharyGoshen/cifar-10-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZacharyHimmelberger/image-classification-for-technical-indicators
https://github.com/Zachdr1/CNNs
https://github.com/Zachdr1/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Zachdr1/GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ZackPashkin/CvT-convolutional-transformer-pytorch
https://github.com/ZackPashkin/YOLOv3-EfficientNet-EffYolo
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ZackPashkin/text2cartoon-pytorch-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Ze-Yang/Context-Transformer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zehaos/MobileNet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Zelgunn/CustomKerasLayers
Fixup Initialization: Residual Learning Without Normalization
https://github.com/Zernach/Fashion-MNIST
https://github.com/Zhachory1/MusicNST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZhangYiqun018/StickerConv
https://github.com/ZhaohuiXue/A-SPN-release
https://github.com/ZhenJie-Zhang/darknet_shoe
https://github.com/ZhimingZhou/AM-GAN
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
https://github.com/Zhiwei-Z/PrompLimitTest
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Zhiwei-Z/SeqPromp
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Zhiwei-Z/pba_experiment
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
https://github.com/Zhiwei-Z/prompzzw
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Zhongying-Deng/NorMatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.