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 28762900 of 10420 papers

TitleStatusHype
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Design of Kernels in Convolutional Neural Networks for Image ClassificationCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Designing Stable Neural Networks using Convex Analysis and ODEsCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
Designing Neural Network Architectures using Reinforcement LearningCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Fairness in Image Classification via SketchingCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclusteringCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Adaptive Stochastic Weight AveragingCode0
Depth and Representation in Vision ModelsCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Deployment of Image Analysis Algorithms under Prevalence ShiftsCode0
Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual DescriptorCode0
Show:102550
← PrevPage 116 of 417Next →

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