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 32263250 of 10420 papers

TitleStatusHype
Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty CalibrationCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
ByzShield: An Efficient and Robust System for Distributed TrainingCode0
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And DatasetCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
DAGNet: A Dual-View Attention-Guided Network for Efficient X-ray Security InspectionCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Deep Neural Networks Motivated by Partial Differential EquationsCode0
Batch-Shaping for Learning Conditional Channel Gated NetworksCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Batch-normalized Maxout Network in NetworkCode0
Improving Fairness in Image Classification via SketchingCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Batch Model Consolidation: A Multi-Task Model Consolidation FrameworkCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Deep Neural Network Compression for Image Classification and Object DetectionCode0
Deep neural network based on F-neurons and its learningCode0
A multiple-instance densely-connected ConvNet for aerial scene classificationCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
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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