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/AndrewAtanov/simclr-pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/AndrewTal/MoCo-Pytorch
Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/AndreyBesedin/ImageClassification
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/AndrzejMiskow/FER-with-Attention-and-Objective-Activation-Functions
https://github.com/Andy-Dang12/Activation-Functions
https://github.com/AngryCai/BS-Nets
BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image
https://github.com/AngusG/bn-advex-zhang-fixup
https://github.com/AngusG/cleverhans-attacking-bnns
https://github.com/Aniket7/Transfer-Learning
CNN Features off-the-shelf: an Astounding Baseline for Recognition
https://github.com/Aniket7/VGGNet-CNN-architecture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Aniket84/Object-Recognition-
https://github.com/Anil1331/DenseNet
https://github.com/AnindKiran/Object-Recognition-Using-CNNs
https://github.com/AnirudhAchal/Human-Parsing
https://github.com/AnirudhMaiya/RAdam
https://github.com/AnishDelft/ModelCompression
https://github.com/AnjieZheng/OctaveConv-Pytorch
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/AnkurDeria/HSI-Traditional-to-Deep-Models
Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
https://github.com/Ankush96/grad-cam.tensorflow
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AnnaManasyan/VICReg
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
https://github.com/AnneDirkson/FuzzyBIO
https://github.com/Anou9531/Bayesian-CNN
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/AntixK/PyTorch-Model-Compare
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/AntreasAntoniou/Learning_to_Learn_via_Self-Critique
https://github.com/AntreasAntoniou/MatchingNetworks
https://github.com/AnudeepKonda/MIMII_anamoly_detection
https://github.com/Anuranjan-Kumar/kaggle_cats-vs-dogs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AnzorGozalishvili/autoencoders_playground
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
https://github.com/ApexPredator1/DenseNet_tensorflow
https://github.com/Arcady1/Doodle-Recognition-Web
https://github.com/ArdhenduBehera/cap
Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification
https://github.com/Argaadya/intro-bayesian
Practical Bayesian Optimization of Machine Learning Algorithms
https://github.com/Arghyadeep/Image-Colorization-using-CNN
https://github.com/AriChow/error_propagation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.