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 35013525 of 10420 papers

TitleStatusHype
Multi-scale Processing of Noisy Images using Edge Preservation LossesCode0
Patch-based Convolutional Neural Network for Whole Slide Tissue Image ClassificationCode0
Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image ClassificationCode0
Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problemsCode0
Soft ascent-descent as a stable and flexible alternative to floodingCode0
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time DetectionCode0
Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image ClassificationCode0
Path-Level Network Transformation for Efficient Architecture SearchCode0
A variable metric proximal stochastic gradient method: an application to classification problemsCode0
Impact of ImageNet Model Selection on Domain AdaptationCode0
Deep Ensembling of Multiband Images for Earth Remote Sensing and Foramnifera DataCode0
Auxiliary Task Update Decomposition: The Good, The Bad and The NeutralCode0
Implicit Generative Prior for Bayesian Neural NetworksCode0
iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-MixerCode0
Emergent symbolic language based deep medical image classificationCode0
Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lagCode0
Immiscible Color Flows in Optimal Transport Networks for Image ClassificationCode0
EMNIST: an extension of MNIST to handwritten lettersCode0
Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuseCode0
Improved Gradient based Adversarial Attacks for Quantized NetworksCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Calibrated Top-1 Uncertainty estimates for classification by score based modelsCode0
ImageNet Classification with Deep Convolutional Neural NetworksCode0
Empirically Measuring Concentration: Fundamental Limits on Intrinsic RobustnessCode0
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGACode0
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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