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 23262350 of 10420 papers

TitleStatusHype
Comparative Evaluation of Clustered Federated Learning MethodsCode0
A Group-Theoretic Framework for Data AugmentationCode0
Invariant backpropagation: how to train a transformation-invariant neural networkCode0
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical HeterogeneityCode0
Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimationCode0
Lacunarity Pooling Layers for Plant Image Classification using Texture AnalysisCode0
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging ClassificationCode0
Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image ClassificationCode0
CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural NetworksCode0
Compact Global Descriptor for Neural NetworksCode0
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-raysCode0
A Dynamic Reduction Network for Point CloudsCode0
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual ClassificationCode0
Compact Bilinear PoolingCode0
Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image ClassificationCode0
InterpNET: Neural Introspection for Interpretable Deep LearningCode0
Interferometric Neural NetworksCode0
A Baseline for Few-Shot Image ClassificationCode0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
Interlocking Backpropagation: Improving depthwise model-parallelismCode0
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)Code0
Intra-class Patch Swap for Self-DistillationCode0
Instance Temperature Knowledge DistillationCode0
Instilling Inductive Biases with SubnetworksCode0
Network DeconvolutionCode0
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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