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 7726–7750 of 10420 papers

TitleStatusHype
Quantum-classical convolutional neural networks in radiological image classification—0
R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut—0
Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer—0
Quantum Enhanced Filter: QFilter—0
Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting—0
Quantum Entanglement in Deep Learning Architectures—0
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding—0
Flatten: Video Action Recognition is an Image Classification task—0
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification—0
Connecting metrics for shape-texture knowledge in computer vision—0
A Free-Energy Principle for Representation Learning—0
Real-time convolutional networks for sonar image classification in low-power embedded systems—0
Quantum Phase Recognition using Quantum Tensor Networks—0
Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines—0
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape—0
Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks—0
Quasi-Newton Optimization Methods For Deep Learning Applications—0
Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning—0
CongNaMul: A Dataset for Advanced Image Processing of Soybean Sprouts—0
A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers—0
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection—0
A Smart System for Selection of Optimal Product Images in E-Commerce—0
Confusable Learning for Large-class Few-Shot Classification—0
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How—0
A Framework using Contrastive Learning for Classification with Noisy Labels—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified