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 6201–6225 of 10420 papers

TitleStatusHype
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy LabelsCode0
GCCN: Global Context Convolutional Network—0
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion—0
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification TaskCode0
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis—0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training ExamplesCode0
Multi-concept adversarial attacks—0
When in Doubt, Summon the Titans: Efficient Inference with Large Models—0
An Adaptive Sampling and Edge Detection Approach for Encoding Static Images for Spiking Neural Networks—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
DARTS for Inverse Problems: a Study on Stability—0
EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks—0
Deep CNNs for Peripheral Blood Cell Classification—0
Online Continual Learning Via Candidates Voting—0
Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix—0
Network Augmentation for Tiny Deep Learning—0
Contrastive Learning of Visual-Semantic Embeddings—0
TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural NetworksCode0
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher—0
Neural Network Pruning Through Constrained Reinforcement Learning—0
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification—0
Trade-offs of Local SGD at Scale: An Empirical Study—0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification—0
Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNetCode0
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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