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 19261950 of 10420 papers

TitleStatusHype
Tent: Fully Test-time Adaptation by Entropy MinimizationCode1
LSD-C: Linearly Separable Deep ClustersCode1
Improving accuracy and speeding up Document Image Classification through parallel systemsCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant WeightsCode1
Neural Ensemble Search for Uncertainty Estimation and Dataset ShiftCode1
Multiscale Deep Equilibrium ModelsCode1
Depth Uncertainty in Neural NetworksCode1
Stream-51: Streaming Classification and Novelty Detection from VideosCode1
Bootstrap your own latent: A new approach to self-supervised LearningCode1
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
On Second Order Behaviour in Augmented Neural ODEsCode1
SegNBDT: Visual Decision Rules for SegmentationCode1
VirTex: Learning Visual Representations from Textual AnnotationsCode1
Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and BeyondCode1
Interferometric Graph Transform: a Deep Unsupervised Graph RepresentationCode1
Interpolation between Residual and Non-Residual NetworksCode1
Object Segmentation Without Labels with Large-Scale Generative ModelsCode1
Multi-view Contrastive Learning for Online Knowledge DistillationCode1
Pick-Object-Attack: Type-Specific Adversarial Attack for Object DetectionCode1
Visual Transformers: Token-based Image Representation and Processing for Computer VisionCode1
Principled learning method for Wasserstein distributionally robust optimization with local perturbationsCode1
Weight Pruning via Adaptive Sparsity LossCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Non-Local Neural Networks With Grouped Bilinear Attentional TransformsCode1
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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