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 84018425 of 10420 papers

TitleStatusHype
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
Evaluating ResNeXt Model Architecture for Image ClassificationCode0
Language-biased image classification: evaluation based on semantic representationsCode0
Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 ClassificationCode0
Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution TrainingCode0
A Benchmark for Interpretability Methods in Deep Neural NetworksCode0
Making Reliable and Flexible Decisions in Long-tailed ClassificationCode0
Making Robust Generalizers Less Rigid with Loss ConcentrationCode0
Core Tokensets for Data-efficient Sequential Training of TransformersCode0
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and TreeCode0
Evaluating Cell Type Inference in Vision Language Models Under Varying Visual ContextCode0
Generate, Annotate, and Learn: NLP with Synthetic TextCode0
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training ExamplesCode0
On the Relationship between Self-Attention and Convolutional LayersCode0
Probabilistic Trust Intervals for Out of Distribution DetectionCode0
Cooperative Meta-Learning with Gradient AugmentationCode0
Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLMCode0
Convolution with even-sized kernels and symmetric paddingCode0
EVA: Exploring the Limits of Masked Visual Representation Learning at ScaleCode0
Revisiting Orthogonality Regularization: A Study for Convolutional Neural Networks in Image ClassificationCode0
EVA-CLIP-18B: Scaling CLIP to 18 Billion ParametersCode0
Convolution Based Spectral Partitioning Architecture for Hyperspectral Image ClassificationCode0
Generating Multi-Center Classifier via Conditional Gaussian DistributionCode0
Generating Natural Adversarial ExamplesCode0
Generating Natural Language Adversarial Examples through Probability Weighted Word SaliencyCode0
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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