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 6826–6850 of 10420 papers

TitleStatusHype
Learning Invariances in Neural NetworksCode1
Ultimate Limits of Thermal Pattern Recognition—0
Boosting Gradient for White-Box Adversarial Attacks—0
What is Wrong with Continual Learning in Medical Image Segmentation?—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Learning Curves for Analysis of Deep NetworksCode0
Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image TranslationCode1
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training SamplesCode0
Cross-Modal Information Maximization for Medical Imaging: CMIM—0
AdaBelief Optimizer: Adapting Stepsizes by theBelief in Observed Gradients—0
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs—0
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks—0
RobustBench: a standardized adversarial robustness benchmarkCode1
Multiclass Wound Image Classification using an Ensemble Deep CNN-based ClassifierCode0
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks—0
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going BeyondCode0
PseudoSeg: Designing Pseudo Labels for Semantic SegmentationCode1
Deep and interpretable regression models for ordinal outcomesCode0
A general approach to compute the relevance of middle-level input features—0
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Performance evaluation and application of computation based low-cost homogeneous machine learning model algorithm for image classification—0
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?—0
The Deep Bootstrap Framework: Good Online Learners are Good Offline GeneralizersCode1
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification TaskCode0
Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling—0
Show:102550
← PrevPage 274 of 417Next →
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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified