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 997610000 of 10420 papers

TitleStatusHype
ThreshNet: An Efficient DenseNet Using Threshold Mechanism to Reduce ConnectionsCode0
Toward Efficient Convolutional Neural Networks With Structured Ternary PatternsCode0
Three things everyone should know about Vision TransformersCode0
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and SegmentationCode0
Toward Metrics for Differentiating Out-of-Distribution SetsCode0
Visual Navigation of Digital Libraries: Retrieval and Classification of Images in the National Library of Norway's Digitised Book CollectionCode0
sharpDARTS: Faster and More Accurate Differentiable Architecture SearchCode0
Scaling Vision Transformers to 22 Billion ParametersCode0
Unleashing the Potential of Synthetic Images: A Study on Histopathology Image ClassificationCode0
Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering ApproachCode0
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral ImagesCode0
What's Hidden in a Randomly Weighted Neural Network?Code0
This actually looks like that: Proto-BagNets for local and global interpretability-by-designCode0
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning settingCode0
Spectral Metric for Dataset Complexity AssessmentCode0
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated LearningCode0
ShaResNet: reducing residual network parameter number by sharing weightsCode0
Towards Analyzing Semantic Robustness of Deep Neural NetworksCode0
Unraveling the Hessian: A Key to Smooth Convergence in Loss Function LandscapesCode0
Towards a text-based quantitative and explainable histopathology image analysisCode0
Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR EmbeddingsCode0
UnseenNet: Fast Training Detector for Any Unseen ConceptCode0
The Uncanny Similarity of Recurrence and DepthCode0
Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image ClassificationCode0
Unsqueeze [CLS] Bottleneck to Learn Rich RepresentationsCode0
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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