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 44514475 of 10420 papers

TitleStatusHype
Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image ClassificationCode0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical DiagnosisCode0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
FLARE up your data: Diffusion-based Augmentation Method in Astronomical ImagingCode0
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer LearningCode0
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional ElementsCode0
Fixup Initialization: Residual Learning Without NormalizationCode0
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and BeyondCode0
Conformal Structured PredictionCode0
Geo-Aware Networks for Fine-Grained RecognitionCode0
Continual Adaptation of Vision Transformers for Federated LearningCode0
Activations and Gradients Compression for Model-Parallel TrainingCode0
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual RecognitionCode0
Spatial Graph Convolutional NetworksCode0
Confident Multiple Choice LearningCode0
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image ClassificationCode0
Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover ClassificationCode0
Hiera: A Hierarchical Vision Transformer without the Bells-and-WhistlesCode0
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier DomainCode0
Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail RecognitionCode0
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image ClassifiersCode0
FishNet: A Versatile Backbone for Image, Region, and Pixel Level PredictionCode0
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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