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 44264450 of 10420 papers

TitleStatusHype
Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image ClassificationCode0
Generating Relevant Counter-Examples from a Positive Unlabeled Dataset for Image ClassificationCode0
Hierarchically Structured Meta-learningCode0
Hierarchical Representations for Efficient Architecture SearchCode0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and BeyondCode0
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class ClassificationCode0
Hiera: A Hierarchical Vision Transformer without the Bells-and-WhistlesCode0
Invariant Shape Representation Learning For Image ClassificationCode0
Connectivity-Inspired Network for Context-Aware RecognitionCode0
Connection Reduction of DenseNet for Image RecognitionCode0
FLIP Reasoning ChallengeCode0
ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic SegmentationCode0
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training QuantizationCode0
Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical ImagesCode0
Active Convolution: Learning the Shape of Convolution for Image ClassificationCode0
Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learningCode0
Continual Adaptation of Vision Transformers for Federated LearningCode0
Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover ClassificationCode0
Generative Modeling Helps Weak Supervision (and Vice Versa)Code0
High Definition image classification in Geoscience using Machine LearningCode0
Why Random Pruning Is All We Need to Start SparseCode0
HaSPeR: An Image Repository for Hand Shadow Puppet RecognitionCode0
FLARE up your data: Diffusion-based Augmentation Method in Astronomical ImagingCode0
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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