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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9901–9925 of 10420 papers

TitleStatusHype
DATA: Differentiable ArchiTecture ApproximationCode0
ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual ClassificationCode0
ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image ClassificationCode0
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasetsCode0
Balanced Mixture of SuperNets for Learning the CNN Pooling ArchitectureCode0
Lets keep it simple, Using simple architectures to outperform deeper and more complex architecturesCode0
Let the Quantum Creep In: Designing Quantum Neural Network Models by Gradually Swapping Out Classical ComponentsCode0
Data-dependent Initializations of Convolutional Neural NetworksCode0
One Man's Trash is Another Man's Treasure: Resisting Adversarial Examples by Adversarial ExamplesCode0
Balanced joint maximum mean discrepancy for deep transfer learningCode0
Frequency maps reveal the correlation between Adversarial Attacks and Implicit BiasCode0
Revisiting Batch Normalization For Practical Domain AdaptationCode0
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning SystemsCode0
Leveraging Content and Context Cues for Low-Light Image EnhancementCode0
Balanced Binary Neural Networks with Gated ResidualCode0
Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment AnalysisCode0
One Prototype Is Enough: Single-Prototype Activation for Interpretable Image ClassificationCode0
Relational Concept Bottleneck ModelsCode0
Asymptotic Soft Filter Pruning for Deep Convolutional Neural NetworksCode0
One-shot Federated Learning without Server-side TrainingCode0
Image recognition from raw labels collected without annotatorsCode0
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural NetworksCode0
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image UnderstandingCode0
Bag of Tricks for Retail Product Image ClassificationCode0
One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention MechanismCode0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified