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

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Showing 9176–9200 of 10420 papers

TitleStatusHype
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning—0
FedDropoutAvg: Generalizable federated learning for histopathology image classification—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Federated Deep Learning with Bayesian Privacy—0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis—0
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification—0
Federated Learning for Commercial Image Sources—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat—0
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis—0
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations—0
Federated Learning Versus Classical Machine Learning: A Convergence Comparison—0
Federated Learning with Bayesian Differential Privacy—0
Federated Learning with Downlink Device Selection—0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—0
Federated Model Search via Reinforcement Learning—0
Federated Multi-Target Domain Adaptation—0
Federated Variational Inference: Towards Improved Personalization and Generalization—0
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion—0
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise—0
FedNS: Improving Federated Learning for collaborative image classification on mobile clients—0
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models—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