Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/WXinlong/DenseCL
Dense Contrastive Learning for Self-Supervised Visual Pre-Training
https://github.com/Waleed-Mujahid/Fast-Gradient-Sign-method
https://github.com/WangFeng18/Swin-Transformer
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/WangMian-Maker/Flask
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/WangTianduo/Prototypical-Networks
https://github.com/Wangxy2180/darknetKinectDetect
https://github.com/WarClans612/deep_learning_final
https://github.com/WaretleR/SelfSupervision
Unsupervised Representation Learning by Predicting Image Rotations
https://github.com/Watson-BCA/HumanPostureRecognition
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/WayneDW/Variance_Reduced_Replica_Exchange_SGMCMC
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
https://github.com/WeiChengTseng/Pytorch-PCGrad
https://github.com/WeiHongLee/Learning-to-impute
Learning to Impute: A General Framework for Semi-supervised Learning
https://github.com/WenbinLee/DN4
https://github.com/WenjieDu/PyPOTS
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/Wenzhima66/readme
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/WessZumino/expectation_propagation
https://github.com/WessZumino/meanfield-theory-of-activation-functions
https://github.com/Westlake-AI/AutoMix
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers
https://github.com/Whole-Brain/mnist-reader
https://github.com/WilliamPeng38/Fashion
https://github.com/WilliamRo/gam_rhn
https://github.com/Wizaron/coord-conv-pytorch
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
https://github.com/WonJunPark/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Wordbe/GradCAM3D
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Wordbe/MobileNetV2
https://github.com/X-PLUG/mPLUG-2
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/XG293/SupConLoss
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/XYZ387/QuaternionCNN_Keras
https://github.com/XavierCHEN34/ClickSEG
https://github.com/Xephyrum/Agriculturice
https://github.com/Xephyrum/CropsMD
https://github.com/XiafeiYu/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Xianchao-Wu/perceiver-pytorch
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://github.com/XiaowanLi2018/TimeSeriesPrediction_BasedOnCNN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/XifengGuo/CapsNet-Keras
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.