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 77017725 of 10420 papers

TitleStatusHype
PoET-BiN: Power Efficient Tiny Binary Neurons0
Communication-Efficient Edge AI: Algorithms and Systems0
An Optimization and Generalization Analysis for Max-Pooling Networks0
Stochasticity in Neural ODEs: An Empirical StudyCode1
Introducing Fuzzy Layers for Deep Learning0
Towards Robust and Reproducible Active Learning Using Neural NetworksCode1
Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization0
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation0
A Toolkit for Generating Code Knowledge GraphsCode1
Byzantine-resilient Decentralized Stochastic Gradient Descent0
MaxUp: A Simple Way to Improve Generalization of Neural Network TrainingCode0
KaoKore: A Pre-modern Japanese Art Facial Expression DatasetCode1
Scalable Second Order Optimization for Deep LearningCode0
A survey on Semi-, Self- and Unsupervised Learning for Image Classification0
Deep regularization and direct training of the inner layers of Neural Networks with Kernel FlowsCode0
Interpreting Interpretations: Organizing Attribution Methods by Criteria0
Algorithm-hardware Co-design for Deformable ConvolutionCode1
TensorShield: Tensor-based Defense Against Adversarial Attacks on Images0
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient DescentCode0
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation0
Uncertainty Estimation in Autoregressive Structured Prediction0
Photonic convolutional neural networks using integrated diffractive optics0
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid CellsCode1
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance LearningCode1
Show:102550
← PrevPage 309 of 417Next →

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