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 251300 of 10419 papers

TitleStatusHype
MetaFormer: A Unified Meta Framework for Fine-Grained RecognitionCode2
A Self-Supervised Descriptor for Image Copy DetectionCode2
ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and BeyondCode2
VOS: Learning What You Don't Know by Virtual Outlier SynthesisCode2
When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention MechanismCode2
UniFormer: Unifying Convolution and Self-attention for Visual RecognitionCode2
ERS: a novel comprehensive endoscopy image dataset for machine learning, compliant with the MST 3.0 specificationCode2
Omnivore: A Single Model for Many Visual ModalitiesCode2
Multi-Representation Adaptation Network for Cross-domain Image ClassificationCode2
Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple SourcesCode2
Vision Transformer with Deformable AttentionCode2
A Simple Episodic Linear Probe Improves Visual Recognition in the WildCode2
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
MetaFormer Is Actually What You Need for VisionCode2
Attention Mechanisms in Computer Vision: A SurveyCode2
TorchXRayVision: A library of chest X-ray datasets and modelsCode2
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationCode2
Momentum Centering and Asynchronous Update for Adaptive Gradient MethodsCode2
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision TransformerCode2
UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene ImageryCode2
LibFewShot: A Comprehensive Library for Few-shot LearningCode2
CrypTen: Secure Multi-Party Computation Meets Machine LearningCode2
AutoFormer: Searching Transformers for Visual RecognitionCode2
BEiT: BERT Pre-Training of Image TransformersCode2
Knowledge distillation: A good teacher is patient and consistentCode2
Beyond Self-attention: External Attention using Two Linear Layers for Visual TasksCode2
Swin Transformer: Hierarchical Vision Transformer using Shifted WindowsCode2
Involution: Inverting the Inherence of Convolution for Visual RecognitionCode2
Learning Transferable Visual Models From Natural Language SupervisionCode2
ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural NetworksCode2
LambdaNetworks: Modeling Long-Range Interactions Without AttentionCode2
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text SupervisionCode2
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetCode2
Bottleneck Transformers for Visual RecognitionCode2
Simplifying Object Segmentation with PixelLib LibraryCode2
RepVGG: Making VGG-style ConvNets Great AgainCode2
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed GradientsCode2
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
Generative Pretraining from PixelsCode2
Unsupervised Learning of Visual Features by Contrasting Cluster AssignmentsCode2
An Overview of Deep Semi-Supervised LearningCode2
SCAN: Learning to Classify Images without LabelsCode2
Supervised Contrastive LearningCode2
ktrain: A Low-Code Library for Augmented Machine LearningCode2
X3D: Expanding Architectures for Efficient Video RecognitionCode2
Binary Neural Networks: A SurveyCode2
Fixing the train-test resolution discrepancy: FixEfficientNetCode2
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic SegmentationCode2
A Simple Framework for Contrastive Learning of Visual RepresentationsCode2
FixMatch: Simplifying Semi-Supervised Learning with Consistency and ConfidenceCode2
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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