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 9276–9300 of 10420 papers

TitleStatusHype
Focused Active Learning for Histopathological Image Classification—0
Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery—0
FolkTalent: Enhancing Classification and Tagging of Indian Folk Paintings—0
Food Classification using Joint Representation of Visual and Textual Data—0
Food Image Classification and Segmentation with Attention-based Multiple Instance Learning—0
Fooling a Real Car with Adversarial Traffic Signs—0
Fooling Neural Networks for Motion Forecasting via Adversarial Attacks—0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks—0
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation—0
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks—0
Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis—0
Forgetting Order of Continual Learning: Examples That are Learned First are Forgotten Last—0
Formal Verification of Deep Neural Networks for Object Detection—0
FORML: Learning to Reweight Data for Fairness—0
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks—0
Forward-Forward Algorithm for Hyperspectral Image Classification: A Preliminary Study—0
Forward-Forward Contrastive Learning—0
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models—0
FoveaTer: Foveated Transformer for Image Classification—0
FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound—0
FPGA-based Acceleration of Neural Network for Image Classification using Vitis AI—0
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks—0
Fractional Wavelet Scattering Network and Applications—0
Framework Construction of an Adversarial Federated Transfer Learning Classifier—0
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks—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
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