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 43764400 of 10420 papers

TitleStatusHype
How Do Training Methods Influence the Utilization of Vision Models?Code0
Why Random Pruning Is All We Need to Start SparseCode0
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means FeaturesCode0
Forget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image ClassificationCode0
Contrastive Learning for OOD in Object detectionCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression ValuesCode0
Histopathological Image Classification using Discriminative Feature-oriented Dictionary LearningCode0
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical ImagingCode0
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networksCode0
Forced Spatial Attention for Driver Foot Activity ClassificationCode0
Ablation study of self-supervised learning for image classificationCode0
General Greedy De-bias LearningCode0
High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature MapsCode0
Histogram Layers for Neural Engineered FeaturesCode0
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image ClassifiersCode0
ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias MitigationCode0
Assessing Sample Quality via the Latent Space of Generative ModelsCode0
Improving the Gating Mechanism of Recurrent Neural NetworksCode0
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification TasksCode0
High Definition image classification in Geoscience using Machine LearningCode0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
Constrained Linear Data-feature Mapping for Image ClassificationCode0
Food Image Recognition by Using Convolutional Neural Networks (CNNs)Code0
Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image ClassificationCode0
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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