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 10351–10400 of 10420 papers

TitleStatusHype
LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images—0
M3amba: Memory Mamba is All You Need for Whole Slide Image Classification—0
MAAM: A Lightweight Multi-Agent Aggregation Module for Efficient Image Classification Based on the MindSpore Framework—0
MABViT -- Modified Attention Block Enhances Vision Transformers—0
Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Building—0
Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review—0
Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review—0
Machine Learning for Brain Disorders: Transformers and Visual Transformers—0
Machine learning for option pricing: an empirical investigation of network architectures—0
Machine Learning Techniques to Detect and Characterise Whistler Radio Waves—0
Machine Learning with DBOS—0
Machine learning with limited data—0
Machine learning with tree tensor networks, CP rank constraints, and tensor dropout—0
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
mAedesID: Android Application for Aedes Mosquito Species Identification using Convolutional Neural Network—0
Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers—0
Magnifying Networks for Images with Billions of Pixels—0
Maintaining Performance with Less Data—0
Make A Long Image Short: Adaptive Token Length for Vision Transformers—0
Make A Long Image Short: Adaptive Token Length for Vision Transformers—0
Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models—0
Making Vision Transformers Truly Shift-Equivariant—0
Mako: Semi-supervised continual learning with minimal labeled data via data programming—0
Malaria detection from RBC images using shallow Convolutional Neural Networks—0
Mamba base PKD for efficient knowledge compression—0
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection—0
Manifold-based Test Generation for Image Classifiers—0
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS—0
Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification—0
Manifold regularization based on Nyström type subsampling—0
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking—0
Mapping Biological Neuron Dynamics into an Interpretable Two-layer Artificial Neural Network—0
Mapping Generative Models onto a Network of Digital Spiking Neurons—0
Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware—0
Margin-Based Regularization and Selective Sampling in Deep Neural Networks—0
Markerless retro-identification complements re-identification of individual insect subjects in archived image data of biological experiments—0
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification—0
mart mobile application to recognize tomato leaf diseases using Convolutional Neural Networks—0
MARVIN: An Open Machine Learning Corpus and Environment for Automated Machine Learning Primitive Annotation and Execution—0
MaskConnect: Connectivity Learning by Gradient Descent—0
Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism—0
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle—0
Masked Siamese ConvNets—0
Massively Scaling Heteroscedastic Classifiers—0
Matching Distributions via Optimal Transport for Semi-Supervised Learning—0
Matching Feature Sets for Few-Shot Image Classification—0
Mathematical Foundation of Interpretable Equivariant Surrogate Models—0
Matrix Is All You Need—0
Matrix Variate RBM Model with Gaussian Distributions—0
Show:102550
← PrevPage 208 of 209Next →
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