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 176200 of 10419 papers

TitleStatusHype
DAT++: Spatially Dynamic Vision Transformer with Deformable AttentionCode2
RevColV2: Exploring Disentangled Representations in Masked Image ModelingCode2
RemoteCLIP: A Vision Language Foundation Model for Remote SensingCode2
MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image ClassificationCode2
NodeFormer: A Scalable Graph Structure Learning Transformer for Node ClassificationCode2
FasterViT: Fast Vision Transformers with Hierarchical AttentionCode2
Efficient Multi-Scale Attention Module with Cross-Spatial LearningCode2
Unicom: Universal and Compact Representation Learning for Image RetrievalCode2
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual RecognitionCode2
Your Diffusion Model is Secretly a Zero-Shot ClassifierCode2
BiFormer: Vision Transformer with Bi-Level Routing AttentionCode2
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical DocumentsCode2
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale AttentionCode2
Stabilizing Transformer Training by Preventing Attention Entropy CollapseCode2
RoPAWS: Robust Semi-supervised Representation Learning from Uncurated DataCode2
MedViT: A Robust Vision Transformer for Generalized Medical Image ClassificationCode2
Stitchable Neural NetworksCode2
Simple Hardware-Efficient Long Convolutions for Sequence ModelingCode2
Understanding Why ViT Trains Badly on Small Datasets: An Intuitive PerspectiveCode2
Effective Data Augmentation With Diffusion ModelsCode2
Class-Incremental Learning: A SurveyCode2
SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual RecognitionCode2
Reversible Column NetworksCode2
MIC: Masked Image Consistency for Context-Enhanced Domain AdaptationCode2
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text LabelsCode2
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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