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 85768600 of 10420 papers

TitleStatusHype
Efficient Search for Customized Activation Functions with Gradient DescentCode0
Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?Code0
AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language ModelsCode0
Open-source FPGA-ML codesign for the MLPerf Tiny BenchmarkCode0
Contextual Encoder-Decoder Network for Visual Saliency PredictionCode0
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?Code0
A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image ClassificationCode0
On the Limitations of Temperature Scaling for Distributions with OverlapsCode0
Open-vocabulary vs. Closed-set: Best Practice for Few-shot Object Detection Considering Text DescribabilityCode0
A Review of Open-World Learning and Steps Toward Open-World Learning Without LabelsCode0
Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image ClassificationCode0
A Unified View of Masked Image ModelingCode0
Contextual Dropout: An Efficient Sample-Dependent Dropout ModuleCode0
Gravitational-wave selection effects using neural-network classifiersCode0
Greedy Layerwise Learning Can Scale to ImageNetCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
DIRA: Dynamic Domain Incremental Regularised AdaptationCode0
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning AccuracyCode0
GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic RetinopathyCode0
Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel ClassificationCode0
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classificationCode0
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural NetworksCode0
Efficient Hyperdimensional ComputingCode0
Efficient Human-in-the-loop System for Guiding DNNs AttentionCode0
Medical Image Debiasing by Learning Adaptive Agreement from a Biased CouncilCode0
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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