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 7251–7275 of 10420 papers

TitleStatusHype
Performing Image Classification for 10 Different Monkey Species using CNN—0
Image Classification by Reinforcement Learning with Two-State Q-LearningCode1
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion—0
Frequency learning for image classification—0
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image ClassificationCode0
An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification—0
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images—0
A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation ProcessCode0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
End-to-end training of deep kernel map networks for image classification—0
Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)Classification—0
E2GC: Energy-efficient Group Convolution in Deep Neural NetworksCode0
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks—0
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural NetworksCode1
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image ClassificationCode1
Set Based Stochastic Subsampling—0
Learning Data Augmentation with Online Bilevel Optimization for Image ClassificationCode1
Target Consistency for Domain Adaptation: when Robustness meets Transferability—0
Compositional Explanations of NeuronsCode1
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks—0
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box AttacksCode1
Learning Semantically Enhanced Feature for Fine-Grained Image ClassificationCode1
Learning Interclass Relations for Image Classification—0
Normalized Loss Functions for Deep Learning with Noisy LabelsCode1
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified