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 97019725 of 10420 papers

TitleStatusHype
Revealing and Protecting Labels in Distributed TrainingCode0
PREMAP: A Unifying PREiMage APproximation Framework for Neural NetworksCode0
Learning From Brains How to Regularize MachinesCode0
Learning from Children: Improving Image-Caption Pretraining via CurriculumCode0
Towards Context-Agnostic Learning Using Synthetic DataCode0
Learning from Crowds by Modeling Common ConfusionsCode0
Premonition: Using Generative Models to Preempt Future Data Changes in Continual LearningCode0
Noisy Concurrent Training for Efficient Learning under Label NoiseCode0
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image ClassifiersCode0
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclusteringCode0
Sharpen Focus: Learning with Attention Separability and ConsistencyCode0
An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)Code0
Preservation of High Frequency Content for Deep Learning-Based Medical Image ClassificationCode0
Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual DescriptorCode0
Between-class Learning for Image ClassificationCode0
Deep convolutional Gaussian processesCode0
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep LearningCode0
Deep Continuous NetworksCode0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
DeepConsensus: using the consensus of features from multiple layers to attain robust image classificationCode0
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge DistillationCode0
Adversarial Learning for Personalized Tag RecommendationCode0
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural NetworksCode0
Improving Network Slimming with Nonconvex RegularizationCode0
Learning Hyperbolic Representations of Topological FeaturesCode0
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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