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 6026–6050 of 10420 papers

TitleStatusHype
MgNet: A Unified Framework of Multigrid and Convolutional Neural Network—0
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision—0
Logarithmic Lenses: Exploring Log RGB Data for Image Classification—0
An Effective Label Noise Model for DNN Text Classification—0
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces—0
MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation—0
Logit Attenuating Weight Normalization—0
GFCN: A New Graph Convolutional Network Based on Parallel Flows—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification—0
LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models—0
DIME-FM : DIstilling Multimodal and Efficient Foundation Models—0
Causality-aware counterfactual confounding adjustment for feature representations learned by deep models—0
Graph Clustering With Missing Data: Convex Algorithms and Analysis—0
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification—0
Graph Classification with Geometric Scattering—0
Graph-based Representation for Image based on Granular-ball—0
Graph based Label Enhancement for Multi-instance Multi-label learning—0
Co-Training 2L Submodels for Visual Recognition—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
A High-Performance Adaptive Quantization Approach for Edge CNN Applications—0
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition—0
Metastatic Cancer Image Classification Based On Deep Learning Method—0
Co-training 2^L Submodels for Visual Recognition—0
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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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified