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 31513175 of 10420 papers

TitleStatusHype
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic DataCode0
Bayesian Nonparametric Federated Learning of Neural NetworksCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Breast cancer image classification on WSI with spatial correlationsCode0
Robust Collaborative Learning with Linear Gradient OverheadCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Bayesian Neural Networks With Maximum Mean Discrepancy RegularizationCode0
Improving Fairness in Image Classification via SketchingCode0
An Adaptive Method Stabilizing Activations for Enhanced GeneralizationCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Deep regularization and direct training of the inner layers of Neural Networks with Kernel FlowsCode0
Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie StimuliCode0
An accurate detection is not all you need to combat label noise in web-noisy datasetsCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Deep Pyramidal Residual NetworksCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image ClassificationCode0
Deep Online Probability Aggregation ClusteringCode0
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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