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 17011725 of 10419 papers

TitleStatusHype
One-Shot Recognition of Manufacturing Defects in Steel SurfacesCode1
On Exact Bit-level Reversible Transformers Without Changing ArchitecturesCode1
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image ClassificationCode1
Contextual Convolutional Neural NetworksCode1
Online Continual Learning: A Systematic Literature Review of Approaches, Challenges, and BenchmarksCode1
Online Continual Learning in Image Classification: An Empirical SurveyCode1
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot InteractionCode1
CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image ClassificationCode1
On Minimum Discrepancy Estimation for Deep Domain AdaptationCode1
CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image ClassificationCode1
Astroformer: More Data Might not be all you need for ClassificationCode1
On the Performance Analysis of Momentum Method: A Frequency Domain PerspectiveCode1
On the Variance of the Adaptive Learning Rate and BeyondCode1
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy LabelsCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
OODformer: Out-Of-Distribution Detection TransformerCode1
Open-set Adversarial Defense with Clean-Adversarial Mutual LearningCode1
Open Set Recognition using Vision Transformer with an Additional Detection HeadCode1
A Comprehensive Approach to Unsupervised Embedding Learning based on AND AlgorithmCode1
Contextual Transformer Networks for Visual RecognitionCode1
Circumventing Outliers of AutoAugment with Knowledge DistillationCode1
Optimal Representations for Covariate ShiftCode1
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-ScansCode1
Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features SelectionCode1
Deep Complex NetworksCode1
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified