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 19511975 of 10420 papers

TitleStatusHype
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural NetworksCode1
Dendritic Learning-incorporated Vision Transformer for Image RecognitionCode1
Spiking Inception Module for Multi-layer Unsupervised Spiking Neural NetworksCode1
SpinalNet: Deep Neural Network with Gradual InputCode1
Towards Better Accuracy-efficiency Trade-offs: Divide and Co-trainingCode1
SPViT: Enabling Faster Vision Transformers via Soft Token PruningCode1
Squeeze-and-Excitation NetworksCode1
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizeCode1
Delving into Out-of-Distribution Detection with Medical Vision-Language ModelsCode1
Standardized Medical Image Classification across Medical DisciplinesCode1
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response JacobiansCode1
Stereopagnosia: Fooling Stereo Networks with Adversarial PerturbationsCode1
Stochasticity in Neural ODEs: An Empirical StudyCode1
Stochastic Layer-Wise Shuffle: A Good Practice to Improve Vision Mamba TrainingCode1
Are These Birds Similar: Learning Branched Networks for Fine-grained RepresentationsCode1
Stream-51: Streaming Classification and Novelty Detection from VideosCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Deep AutoAugmentCode1
Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain GeneralizationCode1
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
SuperLoss: A Generic Loss for Robust Curriculum LearningCode1
Superpixel-based Knowledge Infusion in Deep Neural Networks for Image ClassificationCode1
Superpixel Image Classification with Graph Attention NetworksCode1
Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision TasksCode1
DenoiseRep: Denoising Model for Representation LearningCode1
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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