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 11011125 of 10419 papers

TitleStatusHype
Towards Better Understanding Attribution MethodsCode1
EXACT: How to Train Your AccuracyCode1
Masked Image Modeling with Denoising ContrastCode1
CLCNet: Rethinking of Ensemble Modeling with Classification Confidence NetworkCode1
A graph-transformer for whole slide image classificationCode1
An Empirical Investigation of Representation Learning for ImitationCode1
Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology ImagesCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task SamplingCode1
When does dough become a bagel? Analyzing the remaining mistakes on ImageNetCode1
Introspective Deep Metric Learning for Image RetrievalCode1
CCMB: A Large-scale Chinese Cross-modal BenchmarkCode1
Investigating and Explaining the Frequency Bias in Image ClassificationCode1
Image Classification With Small Datasets: Overview and BenchmarkCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)Code1
Better plain ViT baselines for ImageNet-1kCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
NeuralEF: Deconstructing Kernels by Deep Neural NetworksCode1
Semantic Information Recovery in Wireless NetworksCode1
Learning to Split for Automatic Bias DetectionCode1
Unlocking High-Accuracy Differentially Private Image Classification through ScaleCode1
PolyLoss: A Polynomial Expansion Perspective of Classification Loss FunctionsCode1
Adaptive Split-Fusion TransformerCode1
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?Code1
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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