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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7351–7375 of 10420 papers

TitleStatusHype
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data—0
Introducing Fuzzy Layers for Deep Learning—0
Performing Image Classification for 10 Different Monkey Species using CNN—0
Peri-Net-Pro: The neural processes with quantified uncertainty for crack patterns—0
Robustness via Deep Low-Rank Representations—0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs—0
Intra-Model Collaborative Learning of Neural Networks—0
Deep Learning: Generalization Requires Deep Compositional Feature Space Design—0
Interventional Black-Box Explanations—0
Personalized Federated Learning with Local Attention—0
Personalized Food Image Classification: Benchmark Datasets and New Baseline—0
Interpreting Undesirable Pixels for Image Classification on Black-Box Models—0
Personalized Semantics Excitation for Federated Image Classification—0
Person Re-identification: Past, Present and Future—0
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization—0
Deep Learning Generalization, Extrapolation, and Over-parameterization—0
Deep Learning Generalization and the Convex Hull of Training Sets—0
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation—0
Adaptive Image Anonymization in the Context of Image Classification with Neural Networks—0
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation—0
Interpreting Interpretations: Organizing Attribution Methods by Criteria—0
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification—0
Interpreting Equivariant Representations—0
Interpreting Bias in the Neural Networks: A Peek Into Representational Similarity—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified