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 85268550 of 10420 papers

TitleStatusHype
On the performance of residual block design alternatives in convolutional neural networks for end-to-end audio classification0
Active Learning Solution on Distributed Edge Computing0
AGAN: Towards Automated Design of Generative Adversarial Networks0
Exploring Self-Supervised Regularization for Supervised and Semi-Supervised LearningCode0
Mixup of Feature Maps in a Hidden Layer for Training of Convolutional Neural Network0
Posterior-Guided Neural Architecture SearchCode0
Densely Connected Search Space for More Flexible Neural Architecture SearchCode0
Database Meets Deep Learning: Challenges and Opportunities0
Understanding More about Human and Machine Attention in Deep Neural Networks0
Automatic estimation of heading date of paddy rice using deep learning0
A simple and effective postprocessing method for image classification0
Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield0
ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object DetectionCode0
The Functional Neural ProcessCode0
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral ImagesCode0
PolSAR Image Classification based on Polarimetric Scattering Coding and Sparse Support Matrix Machine0
Active Generative Adversarial Network for Image Classification0
Equivariant neural networks and equivarificationCode0
Mixture separability loss in a deep convolutional network for image classification0
SELFIE: Refurbishing Unclean Samples for Robust Deep LearningCode0
Adversarial Robustness Assessment: Why both L_0 and L_ Attacks Are NecessaryCode0
Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation0
A Signal Propagation Perspective for Pruning Neural Networks at InitializationCode0
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training0
Deep Learning Development Environment in Virtual RealityCode0
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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