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 23762400 of 10420 papers

TitleStatusHype
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer LearningCode0
InterpNET: Neural Introspection for Interpretable Deep LearningCode0
Adversarial Style Augmentation for Domain Generalized Urban-Scene SegmentationCode0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
ColorNet: Investigating the importance of color spaces for image classificationCode0
ColorNet -- Estimating Colorfulness in Natural ImagesCode0
ColorMAE: Exploring data-independent masking strategies in Masked AutoEncodersCode0
Architectural Vision for Quantum Computing in the Edge-Cloud ContinuumCode0
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image ClassificationCode0
Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such AttacksCode0
ARC: Anchored Representation Clouds for High-Resolution INR ClassificationCode0
Intelligent Multi-View Test Time AugmentationCode0
Interferometric Neural NetworksCode0
Instance-dependent Label Distribution Estimation for Learning with Label NoiseCode0
A Rate-Distortion Framework for Explaining Neural Network DecisionsCode0
Instance Temperature Knowledge DistillationCode0
Adversarial Structure Matching for Structured Prediction TasksCode0
Instilling Inductive Biases with SubnetworksCode0
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image ClassificationCode0
Multi-head Spatial-Spectral Mamba for Hyperspectral Image ClassificationCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
Interlocking Backpropagation: Improving depthwise model-parallelismCode0
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and EstimationCode0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
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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