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 30763100 of 10420 papers

TitleStatusHype
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
Benchmark Generation Framework with Customizable Distortions for Image Classifier RobustnessCode0
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 Fairness in Image Classification via SketchingCode0
Deep transfer learning method based on automatic domain alignment and moment matchingCode0
DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance LevelCode0
Fourier Transform Approximation as an Auxiliary Task for Image ClassificationCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Distance Based Image Classification: A solution to generative classification's conundrum?Code0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification AcceleratorCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
BEiT v2: Masked Image Modeling with Vector-Quantized Visual TokenizersCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Deep Sub-Ensembles for Fast Uncertainty Estimation in Image ClassificationCode0
Anytime Inference with Distilled Hierarchical Neural EnsemblesCode0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
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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