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 37013725 of 10420 papers

TitleStatusHype
ImageNot: A contrast with ImageNet preserves model rankingsCode0
ImageNet Classification with Deep Convolutional Neural NetworksCode0
Automated wildlife image classification: An active learning tool for ecological applicationsCode0
Decision-making and control with diffractive optical networksCode0
Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural networkCode0
Decision Forests, Convolutional Networks and the Models in-BetweenCode0
DecisioNet: A Binary-Tree Structured Neural NetworkCode0
Automated Seed Quality Testing System using GAN & Active LearningCode0
Automated Search for Configurations of Deep Neural Network ArchitecturesCode0
DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and ExplanationCode0
Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracyCode0
Policy-Based Federated LearningCode0
Image Classification with Hierarchical Multigraph NetworksCode0
iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-MixerCode0
Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network EnsembleCode0
Image Classification Using Singular Value Decomposition and OptimizationCode0
Demon: Improved Neural Network Training with Momentum DecayCode0
Image Classification with Classic and Deep Learning TechniquesCode0
Automated Knowledge Distillation via Monte Carlo Tree SearchCode0
Adaptive Adversarial Cross-Entropy Loss for Sharpness-Aware MinimizationCode0
Image classification and retrieval with random depthwise signed convolutional neural networksCode0
Image classification in frequency domain with 2SReLU: a second harmonics superposition activation functionCode0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural NetworksCode0
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain AdaptationCode0
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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