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/Lester1711/DSI15_Capstone
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LiAng199523/Multi-Attention-CNN-pytorch
https://github.com/LiWentomng/OrientedRepPoints
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/LiamLau1/MLDE
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
https://github.com/Lieberk/Paddle-FSL-Baseline
https://github.com/Lieberk/Paddle-FSL-GNN
https://github.com/Lieberk/Paddle-MatchingNet
https://github.com/Lightness7/SSBC
https://github.com/Lily-Le/EcoTTA
EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization
https://github.com/Lin-Zhipeng/CNN-RNN-A-Unified-Framework-for-Multi-label-Image-Classification
CNN-RNN: A Unified Framework for Multi-label Image Classification
https://github.com/LinHuiTeng/TrashClassifier
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Linear95/CLUB
https://github.com/LingaswamyD23/GAN_Using_Pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Little-Frog-233/imagecluster
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LiuXinyu12378/MatchingNetworks
https://github.com/Liuhong99/Imbalanced-SSL
Self-supervised Learning is More Robust to Dataset Imbalance
https://github.com/Liut2016/ecg-supcontrast
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Liuyubao/transfer-learning
https://github.com/LiyuanLucasLiu/LM-LSTM-CRF
https://github.com/Lmath11/darknet
https://github.com/LoJunKai/SUTD_initIAtion
https://github.com/LongLeCE/CoAtNet-PyTorch
CoAtNet: Marrying Convolution and Attention for All Data Sizes
https://github.com/LorisDonovan/GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/LorisNanni/Multi-Band-Image-Analysis-Using-Ensemble-Neural-Networks
https://github.com/Lornatang/DCGAN-PyTorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Lornatang/ResNet-PyTorch
https://github.com/Lornatang/TensorFlow-MNIST
https://github.com/Lornatang/tensorflow-vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LouisFoucard/StereoConvNet
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/LouisFoucard/w-net
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/LourisXu/rfs
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
https://github.com/LsAntonio/60daysofudacity
https://github.com/LuCeHe/lru_unofficial
https://github.com/LuanAdemi/VisualGo
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/LucasElbert/GradCam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.