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 33263350 of 10420 papers

TitleStatusHype
Deep learning with Elastic Averaging SGDCode0
Deep Learning with Eigenvalue Decay RegularizerCode0
Deep Learning using Linear Support Vector MachinesCode0
Balanced Mixture of SuperNets for Learning the CNN Pooling ArchitectureCode0
AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain ShiftCode0
Deep Learning under Privileged Information Using Heteroscedastic DropoutCode0
Balanced joint maximum mean discrepancy for deep transfer learningCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Multiscale Principle of Relevant Information for Hyperspectral Image ClassificationCode0
A Mosquito is Worth 16x16 Larvae: Evaluation of Deep Learning Architectures for Mosquito Larvae ClassificationCode0
Targeted Deep Learning System Boundary TestingCode0
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of GradientsCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence ModelsCode0
Early-exit deep neural networks for distorted images: providing an efficient edge offloadingCode0
Successive Embedding and Classification Loss for Aerial Image ClassificationCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Adaptive Learning Rate and Momentum for Training Deep Neural NetworksCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Balanced Binary Neural Networks with Gated ResidualCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
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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