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 33013325 of 10420 papers

TitleStatusHype
Dynamic Convolution: Attention over Convolution KernelsCode0
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster InferenceCode0
Understanding and Robustifying Differentiable Architecture SearchCode0
Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest RadiographsCode0
Improving Fairness in Image Classification via SketchingCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Deep Manifold Embedding for Hyperspectral Image ClassificationCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Multi-layered tensor networks for image classificationCode0
Deeply-Supervised NetsCode0
A Certified Radius-Guided Attack Framework to Image Segmentation ModelsCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training SamplesCode0
Multilingual Vision-Language Pre-training for the Remote Sensing DomainCode0
Dynamic Loss For Robust LearningCode0
Adaptively Connected Neural NetworksCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Dynamic Mode Decomposition based feature for Image ClassificationCode0
Deep Learning with Gaussian Differential PrivacyCode0
Deep learning with Elastic Averaging SGDCode0
Deep Learning with Eigenvalue Decay RegularizerCode0
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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