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 35763600 of 10420 papers

TitleStatusHype
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural ArchitectureCode0
Classification Metrics for Image Explanations: Towards Building Reliable XAI-EvaluationsCode0
Enhancing Adaptive Deep Networks for Image Classification via Uncertainty-aware Decision FusionCode0
AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural NetworksCode0
iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-MixerCode0
Deep convolutional Gaussian processesCode0
ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image ClassificationCode0
Auto-Precision Scaling for Distributed Deep LearningCode0
ImageNot: A contrast with ImageNet preserves model rankingsCode0
Deep Continuous NetworksCode0
Adaptive Convolution Kernel for Artificial Neural NetworksCode0
Image Quality Assessment Guided Deep Neural Networks TrainingCode0
Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lagCode0
DeepConsensus: using the consensus of features from multiple layers to attain robust image classificationCode0
Deep Competitive Pathway NetworksCode0
Deep Combinatorial AggregationCode0
ImageNet Classification with Deep Convolutional Neural NetworksCode0
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial ExamplesCode0
Provably Near-Optimal Federated Ensemble Distillation with Negligible OverheadCode0
Immiscible Color Flows in Optimal Transport Networks for Image ClassificationCode0
Intra-class Patch Swap for Self-DistillationCode0
Learning Compressed Transforms with Low Displacement RankCode0
Meta-Learning without MemorizationCode0
Deep CNN-based Multi-task Learning for Open-Set RecognitionCode0
Image classification in frequency domain with 2SReLU: a second harmonics superposition activation functionCode0
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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