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/ChristophReich1996/Involution
Involution: Inverting the Inherence of Convolution for Visual Recognition
https://github.com/ChristophReich1996/MaxViT
https://github.com/ChristophReich1996/Swin-Transformer-V2
https://github.com/Chumsy0725/logit-adj-pytorch
https://github.com/ChuuyaZZZ/6787-Final-project
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/CiaoHe/Nex_RSC
https://github.com/Ciprian95/Licenta
https://github.com/Cjl-MedSeg/DenseNet
https://github.com/ClaraGalimberti/HamiltonianNet
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
https://github.com/ClaudiaShu/SSL-FER
https://github.com/Claydon-Wang/OFTTA
Improving robustness against common corruptions by covariate shift adaptation
https://github.com/Cloud-CV/Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/CocoJam/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest
https://github.com/Code-Fight/darknet_study
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/CodeChefVIT/VOID
https://github.com/Coderx7/SimpleNet_Pytorch
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
https://github.com/CodingWitcher/Chexnet_NIH
https://github.com/CodingWitcher/reverse-oldification
https://github.com/CoinCheung/MFM
Masked Frequency Modeling for Self-Supervised Visual Pre-Training
https://github.com/CoinCheung/SphereReID
https://github.com/CoinCheung/denseCL
Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/CoinCheung/fixmatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/CoinCheung/fixmatch-pytorch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/Colonel-Hathi/CapsNet-Treemap-Perception-Trafficsign
https://github.com/ComputerVisionTalks/Talk-01
Learning Data Augmentation with Online Bilevel Optimization for Image Classification
https://github.com/Confusezius/selfsupervised_learning
Deep Clustering for Unsupervised Learning of Visual Features
https://github.com/Congpeiliang/yolo
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/Contamination-Classification/DenseNet
https://github.com/ContinualAI/avalanche
https://github.com/CorneliusHsiao/FoodMethodGAN
https://github.com/CottonBall811/English-Alphabet-OCR
https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork
https://github.com/CryptoSalamander/pytorch_paper_implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/CtrlZ1/Domain-Adaptation-Algorithms
Learning Transferable Features with Deep Adaptation Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.