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 29012925 of 10420 papers

TitleStatusHype
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclusteringCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
Detecting floodwater on roadways from image data with handcrafted features and deep transfer learningCode0
Adaptive Stochastic Weight AveragingCode0
Depth and Representation in Vision ModelsCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Deployment of Image Analysis Algorithms under Prevalence ShiftsCode0
Beyond Uniform Query Distribution: Key-Driven Grouped Query AttentionCode0
Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual DescriptorCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
DENSER: Deep Evolutionary Network Structured RepresentationCode0
Dense open-set recognition with synthetic outliers generated by Real NVPCode0
Biased Attention: Do Vision Transformers Amplify Gender Bias More than Convolutional Neural Networks?Code0
Inference via Sparse Coding in a Hierarchical Vision ModelCode0
DenseNet Models for Tiny ImageNet ClassificationCode0
Between-class Learning for Image ClassificationCode0
Exploring Adversarial Robustness of Vision Transformers in the Spectral PerspectiveCode0
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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