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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7751–7775 of 10420 papers

TitleStatusHype
Deepfake Video Forensics based on Transfer Learning—0
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think—0
Improving Transferability of Deep Neural Networks—0
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks—0
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network—0
Adaptive Dataset Sampling by Deep Policy Gradient—0
RadTex: Learning Efficient Radiograph Representations from Text Reports—0
Improving training of deep neural networks via Singular Value Bounding—0
A Study on Transformer Configuration and Training Objective—0
RAID: Randomized Adversarial-Input Detection for Neural Networks—0
RAILS: A Robust Adversarial Immune-inspired Learning System—0
Deep Epitomic Convolutional Neural Networks—0
Improving the Reliability for Confidence Estimation—0
Deep Ensembles for Low-Data Transfer Learning—0
A Machine-Synesthetic Approach To DDoS Network Attack Detection—0
Improving the Effectiveness of Deep Generative Data—0
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis—0
Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification—0
Randomized kernels for large scale Earth observation applications—0
Randomized Principal Component Analysis for Hyperspectral Image Classification—0
Random Laplace Feature Maps for Semigroup Kernels on Histograms—0
Random Padding Data Augmentation—0
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified