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 576600 of 10419 papers

TitleStatusHype
Curriculum By SmoothingCode1
Asymmetric Polynomial Loss For Multi-Label ClassificationCode1
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised LearningCode1
Asymmetric Loss For Multi-Label ClassificationCode1
Adaptive Split-Fusion TransformerCode1
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecksCode1
Curriculum Temperature for Knowledge DistillationCode1
A Survey on Transferability of Adversarial Examples across Deep Neural NetworksCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
A Survey of Classical And Quantum Sequence ModelsCode1
Learning Loss for Active LearningCode1
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?Code1
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
DEAL: Deep Evidential Active Learning for Image ClassificationCode1
Cross-modulated Few-shot Image Generation for Colorectal Tissue ClassificationCode1
Cross-modal Adversarial ReprogrammingCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network CalibrationCode1
Cross-Layer Retrospective Retrieving via Layer AttentionCode1
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale AttentionCode1
Achieving Fairness Through Channel Pruning for Dermatological Disease DiagnosisCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative LearningCode1
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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