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 6451–6475 of 10420 papers

TitleStatusHype
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection—0
ModelLock: Locking Your Model With a Spell—0
Doubly Convolutional Neural Networks—0
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning—0
Convolutional Neural Networks for Multispectral Image Cloud Masking—0
GeoTop: Advancing Image Classification with Geometric-Topological Analysis—0
Model Specialization for Inference Via End-to-End Distillation, Pruning, and Cascades—0
ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks—0
Active Learning Solution on Distributed Edge Computing—0
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification—0
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks—0
GeoNet: Benchmarking Unsupervised Adaptation across Geographies—0
Geometry aware convolutional filters for omnidirectional images representation—0
Convolutional neural networks compression with low rank and sparse tensor decompositions—0
Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification—0
Geometric Scattering for Graph Data Analysis—0
Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons—0
Asymmetric Duos: Sidekicks Improve Uncertainty—0
Geometric Median Matching for Robust k-Subset Selection from Noisy Data—0
MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images—0
Geometric Mean Improves Loss For Few-Shot Learning—0
Convolutional Neural Networks and Vision Transformers for Fashion MNIST Classification: A Literature Review—0
Agile Modeling: From Concept to Classifier in Minutes—0
Asymmetric Distribution Measure for Few-shot Learning—0
Convolutional Neural Network Pruning Using Filter Attenuation—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