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

Showing 101150 of 10419 papers

TitleStatusHype
K-LITE: Learning Transferable Visual Models with External KnowledgeCode2
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated ConvolutionsCode2
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image ClassificationCode2
Inception TransformerCode2
ktrain: A Low-Code Library for Augmented Machine LearningCode2
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?Code2
GPipe: Efficient Training of Giant Neural Networks using Pipeline ParallelismCode2
Global Context Vision TransformersCode2
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNsCode2
GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language ModelsCode2
An Overview of Deep Semi-Supervised LearningCode2
HGRN2: Gated Linear RNNs with State ExpansionCode2
GIT: A Generative Image-to-text Transformer for Vision and LanguageCode2
GrootVL: Tree Topology is All You Need in State Space ModelCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
Generalized Parametric Contrastive LearningCode2
Frontiers in Intelligent ColonoscopyCode2
Focal Modulation NetworksCode2
FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation LearningCode2
Generative Pretraining from PixelsCode2
LambdaNetworks: Modeling Long-Range Interactions Without AttentionCode2
Big Transfer (BiT): General Visual Representation LearningCode2
A Self-Supervised Descriptor for Image Copy DetectionCode2
A Simple Episodic Linear Probe Improves Visual Recognition in the WildCode2
SCAN: Learning to Classify Images without LabelsCode2
Learning Transferable Visual Models From Natural Language SupervisionCode2
GroupMamba: Efficient Group-Based Visual State Space ModelCode2
Fixing the train-test resolution discrepancy: FixEfficientNetCode2
Fixing the train-test resolution discrepancyCode2
FixMatch: Simplifying Semi-Supervised Learning with Consistency and ConfidenceCode2
Fast Vision Transformers with HiLo AttentionCode2
3D-RCNet: Learning from Transformer to Build a 3D Relational ConvNet for Hyperspectral Image ClassificationCode2
FixMatch: Simplifying Semi-Supervised Learning with Consistency and ConfidenceCode2
ERS: a novel comprehensive endoscopy image dataset for machine learning, compliant with the MST 3.0 specificationCode2
MobileOne: An Improved One millisecond Mobile BackboneCode2
EMR-Merging: Tuning-Free High-Performance Model MergingCode2
FasterViT: Fast Vision Transformers with Hierarchical AttentionCode2
HAIR: Hypernetworks-based All-in-One Image RestorationCode2
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
MogaNet: Multi-order Gated Aggregation NetworkCode2
Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple SourcesCode2
Efficient Multi-Scale Attention Module with Cross-Spatial LearningCode2
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene ImageryCode2
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision ApplicationsCode2
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and TransformerCode2
Effective Data Augmentation With Diffusion ModelsCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
Dilated Neighborhood Attention TransformerCode2
Agent Attention: On the Integration of Softmax and Linear AttentionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified