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 35263550 of 10420 papers

TitleStatusHype
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time DetectionCode0
Employing Sentence Space Embedding for Classification of Data Stream from Fake News DomainCode0
CLAD: A Contrastive Learning based Approach for Background DebiasingCode0
Improved efficient capsule network for Kuzushiji-MNIST benchmark dataset classificationCode0
Soft ascent-descent as a stable and flexible alternative to floodingCode0
Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuseCode0
Implicit Generative Prior for Bayesian Neural NetworksCode0
Class2Str: End to End Latent Hierarchy LearningCode0
Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image ClassificationCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
Improving Fairness in Image Classification via SketchingCode0
iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-MixerCode0
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGACode0
Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lagCode0
Immiscible Color Flows in Optimal Transport Networks for Image ClassificationCode0
Adaptive Cross-Modal Few-Shot LearningCode0
EncodingNet: A Novel Encoding-based MAC Design for Efficient Neural Network AccelerationCode0
ImageNot: A contrast with ImageNet preserves model rankingsCode0
ImageNet Classification with Deep Convolutional Neural NetworksCode0
Image Quality Assessment Guided Deep Neural Networks TrainingCode0
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensembleCode0
DeepCorrect: Correcting DNN models against Image DistortionsCode0
Deep Generalized Convolutional Sum-Product NetworksCode0
Deep Convolutional Neural Networks for Breast Cancer Histology Image AnalysisCode0
ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks PerformanceCode0
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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