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 6526–6550 of 10420 papers

TitleStatusHype
Demystifying Loss Functions for Classification—0
Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty—0
Achieving Explainability in a Visual Hard Attention Model through Content Prediction—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
Recall Loss for Imbalanced Image Classification and Semantic SegmentationCode1
The Bootstrap Framework: Generalization Through the Lens of Online Optimization—0
The Foes of Neural Network’s Data Efficiency Among Unnecessary Input Dimensions—0
Counterfactual Thinking for Long-tailed Information Extraction—0
A Gradient-based Kernel Approach for Efficient Network Architecture Search—0
TwinDNN: A Tale of Two Deep Neural Networks—0
Uncertain Out-of-Domain Generalization—0
Optimal allocation of data across training tasks in meta-learning—0
Context-Agnostic Learning Using Synthetic Data—0
Constructing Multiple High-Quality Deep Neural Networks: A TRUST-TECH Based Approach—0
Conditional Networks—0
Certified robustness against physically-realizable patch attack via randomized cropping—0
Dual-Tree Wavelet Packet CNNs for Image Classification—0
More Side Information, Better Pruning: Shared-Label Classification as a Case Study—0
On the Effectiveness of Deep Ensembles for Small Data Tasks—0
Learning Representation in Colour Conversion—0
ROMUL: Scale Adaptative Population Based Training—0
Sparsifying Networks via Subdifferential Inclusion—0
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS—0
AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Show:102550
← PrevPage 262 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
10RevCol-HTop 1 Accuracy90—Unverified