SOTAVerified

Image Classification

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.

Source: Metamorphic Testing for Object Detection Systems

Papers

Showing 42514275 of 10420 papers

TitleStatusHype
Continual Learning with Deep Streaming Regularized Discriminant AnalysisCode0
Continual Learning with Deep Generative ReplayCode0
How transfer learning is used in generative models for image classification: improved accuracyCode0
Continual Learning of Unsupervised Monocular Depth from VideosCode0
Continual Learning in Open-vocabulary Classification with Complementary Memory SystemsCode0
How to Use Dropout Correctly on Residual Networks with Batch NormalizationCode0
Why Random Pruning Is All We Need to Start SparseCode0
How Do Training Methods Influence the Utilization of Vision Models?Code0
How Flawed Is ECE? An Analysis via Logit SmoothingCode0
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image ClassifiersCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Continual Contrastive Learning for Image ClassificationCode0
Active Generation for Image ClassificationCode0
Continual and Multi-Task Architecture SearchCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Histopathological Image Classification using Discriminative Feature-oriented Dictionary LearningCode0
DeMansia: Mamba Never Forgets Any TokensCode0
A Stochastic Proximal Polyak Step SizeCode0
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networksCode0
Contextual Learning in Fourier Complex Field for VHR Remote Sensing ImagesCode0
Contextualizing Meta-Learning via Learning to DecomposeCode0
Histogram Layers for Neural Engineered FeaturesCode0
Contextual Explanation NetworksCode0
Contextual Encoder-Decoder Network for Visual Saliency PredictionCode0
Contextual Dropout: An Efficient Sample-Dependent Dropout ModuleCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified