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 24012425 of 10420 papers

TitleStatusHype
Learning Hyperparameters via a Data-Emphasized Variational ObjectiveCode0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
Cold Case: The Lost MNIST DigitsCode0
Coherence Awareness in Diffractive Neural NetworksCode0
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)Code0
Interlocking Backpropagation: Improving depthwise model-parallelismCode0
InterpNET: Neural Introspection for Interpretable Deep LearningCode0
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-raysCode0
A Continual Development Methodology for Large-scale Multitask Dynamic ML SystemsCode0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
A Programmable Approach to Neural Network CompressionCode0
Intelligent Multi-View Test Time AugmentationCode0
Coarse-to-Fine Object Tracking Using Deep Features and Correlation FiltersCode0
Instance Temperature Knowledge DistillationCode0
Instilling Inductive Biases with SubnetworksCode0
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image ClassificationCode0
Interferometric Neural NetworksCode0
CoA: Chain-of-Action for Generative Semantic LabelsCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep LearningCode0
CNNtention: Can CNNs do better with Attention?Code0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Input-gradient space particle inference for neural network ensemblesCode0
Rethinking Pre-Trained Feature Extractor Selection in Multiple Instance Learning for Whole Slide Image ClassificationCode0
Input Invex Neural NetworkCode0
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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