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 8326–8350 of 10420 papers

TitleStatusHype
Deep Axial Hypercomplex Networks—0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy—0
Deep Boosting: Layered Feature Mining for General Image Classification—0
Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets—0
Deep CNNs for large scale species classification—0
Deep CNNs for Peripheral Blood Cell Classification—0
Deep Collaborative Learning for Visual Recognition—0
Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks—0
Deep Context-Aware Kernel Networks—0
Deep Convolutional Decision Jungle for Image Classification—0
Deep convolutional networks for pancreas segmentation in CT imaging—0
Deep Convolutional Neural Networks as Generic Feature Extractors—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
Deep Convolutional Spiking Neural Networks for Image Classification—0
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning—0
Deep Curriculum Learning for PolSAR Image Classification—0
Deep Decision Network for Multi-Class Image Classification—0
Deep Degradation Prior for Low-Quality Image Classification—0
Deep Dependency Networks for Multi-Label Classification—0
Deep Dictionary Learning: A PARametric NETwork Approach—0
Deep Discriminative Learning for Unsupervised Domain Adaptation—0
Deep Domain Generalization with Feature-norm Network—0
Deep Elastic Networks with Model Selection for Multi-Task Learning—0
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers—0
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles—0
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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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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