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 6051–6075 of 10420 papers

TitleStatusHype
Learning with Neighbor Consistency for Noisy Labels—0
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems—0
Learning with Label Noise for Image Retrieval by Selecting Interactions—0
Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification—0
Learning with Inadequate and Incorrect Supervision—0
Learning with Hierarchical Complement Objective—0
Learning with Differentiable Algorithms—0
Development Of A Fire Detection System On Satellite Images—0
An Artificial Neural Network for Image Classification Inspired by Aversive Olfactory Learning Circuits in Caenorhabditis Elegans—0
Learning with convolution and pooling operations in kernel methods—0
Continual Learning with Evolving Class Ontologies—0
Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
Low-Memory Neural Network Training: A Technical Report—0
Learning What Data to Learn—0
Low-rank features based double transformation matrices learning for image classification—0
Developing a Recommendation Benchmark for MLPerf Training and Inference—0
Learning Wake-Sleep Recurrent Attention Models—0
Learning Visual Conditioning Tokens to Correct Domain Shift for Fully Test-time Adaptation—0
Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification—0
DetNet: Design Backbone for Object Detection—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
Learning transformer-based heterogeneously salient graph representation for multimodal remote sensing image classification—0
Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum Advantage—0
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified