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 151200 of 10419 papers

TitleStatusHype
Generalized Parametric Contrastive LearningCode2
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNsCode2
FixMatch: Simplifying Semi-Supervised Learning with Consistency and ConfidenceCode2
FasterViT: Fast Vision Transformers with Hierarchical AttentionCode2
FixMatch: Simplifying Semi-Supervised Learning with Consistency and ConfidenceCode2
EMR-Merging: Tuning-Free High-Performance Model MergingCode2
ERS: a novel comprehensive endoscopy image dataset for machine learning, compliant with the MST 3.0 specificationCode2
EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space DualityCode2
Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple SourcesCode2
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
MogaNet: Multi-order Gated Aggregation NetworkCode2
Efficient Multi-Scale Attention Module with Cross-Spatial LearningCode2
Effective Data Augmentation With Diffusion ModelsCode2
UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene ImageryCode2
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and TransformerCode2
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural NetworksCode2
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision ApplicationsCode2
Focal Modulation NetworksCode2
DEYO: DETR with YOLO for End-to-End Object DetectionCode2
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTsCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
Fast Vision Transformers with HiLo AttentionCode2
Fixing the train-test resolution discrepancyCode2
Fixing the train-test resolution discrepancy: FixEfficientNetCode2
Deep PCB To COCO ConvertorCode2
Frontiers in Intelligent ColonoscopyCode2
MobileOne: An Improved One millisecond Mobile BackboneCode2
Accelerating Transformers with Spectrum-Preserving Token MergingCode2
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
Generative Pretraining from PixelsCode2
An Overview of Deep Semi-Supervised LearningCode2
GIT: A Generative Image-to-text Transformer for Vision and LanguageCode2
GrootVL: Tree Topology is All You Need in State Space ModelCode2
GroupMamba: Efficient Group-Based Visual State Space ModelCode2
Decoupled Knowledge DistillationCode2
HGRN2: Gated Linear RNNs with State ExpansionCode2
Agent Attention: On the Integration of Softmax and Linear AttentionCode2
Class-Incremental Learning: A SurveyCode2
Inception TransformerCode2
DataDream: Few-shot Guided Dataset GenerationCode2
DAMamba: Vision State Space Model with Dynamic Adaptive ScanCode2
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image ClassificationCode2
CrypTen: Secure Multi-Party Computation Meets Machine LearningCode2
LambdaNetworks: Modeling Long-Range Interactions Without AttentionCode2
Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image ClassificationCode2
DAT++: Spatially Dynamic Vision Transformer with Deformable AttentionCode2
SCAN: Learning to Classify Images without LabelsCode2
Learning Transferable Visual Models From Natural Language SupervisionCode2
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image ClassificationCode2
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View CompletionCode2
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified