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 85518575 of 10420 papers

TitleStatusHype
MCDAL: Maximum Classifier Discrepancy for Active LearningCode0
Open-Set Face Recognition with Maximal Entropy and Objectosphere LossCode0
Emergent symbolic language based deep medical image classificationCode0
MC-MLP:Multiple Coordinate Frames in all-MLP Architecture for VisionCode0
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring PluginCode0
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature AugmentationCode0
Embedded hyper-parameter tuning by Simulated AnnealingCode0
Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural NetworksCode0
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble ModelsCode0
MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image ClassificationCode0
Contextual Learning in Fourier Complex Field for VHR Remote Sensing ImagesCode0
ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent TrainingCode0
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian SpectrumsCode0
Contextualizing Meta-Learning via Learning to DecomposeCode0
A Certified Radius-Guided Attack Framework to Image Segmentation ModelsCode0
Graph-Based Global Reasoning NetworksCode0
Additive Noise Annealing and Approximation Properties of Quantized Neural NetworksCode0
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image SegmentationCode0
Improving Axial-Attention Network Classification via Cross-Channel Weight SharingCode0
MDMLP: Image Classification from Scratch on Small Datasets with MLPCode0
Contextual Explanation NetworksCode0
EG-Booster: Explanation-Guided Booster of ML Evasion AttacksCode0
eGAN: Unsupervised approach to class imbalance using transfer learningCode0
Measurement noise scaling laws for cellular representation learningCode0
Efficiera Residual Networks: Hardware-Friendly Fully Binary Weight with 2-bit Activation Model Achieves Practical ImageNet AccuracyCode0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified