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 72767300 of 10420 papers

TitleStatusHype
Automatic Open-World Reliability AssessmentCode0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition0
Pixel precise unsupervised detection of viral particle proliferation in cellular imaging data0
A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification0
Ontology-driven Event Type Classification in ImagesCode0
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework0
Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism0
Identifying Mislabeled Images in Supervised Learning Utilizing Autoencoder0
Online Descriptor Enhancement via Self-Labelling Triplets for Visual Data Association0
Channel Pruning via Multi-Criteria based on Weight Dependency0
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction0
Confusable Learning for Large-class Few-Shot Classification0
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion AnalysisCode0
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning0
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGACode0
Deep Active Learning with Augmentation-based Consistency EstimationCode0
Subtensor Quantization for Mobilenets0
Hyperspectral classification of blood-like substances using machine learning methods combined with genetic algorithms in transductive and inductive scenarios0
MACE: Model Agnostic Concept Extractor for Explaining Image Classification NetworksCode0
Image Classification via Quantum Machine Learning0
A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNsCode0
Similarity-Based Clustering for Enhancing Image Classification ArchitecturesCode0
Deep Feature Augmentation for Occluded Image Classification0
FENAS: Flexible and Expressive Neural Architecture Search0
OCR, Classification & Machine Translation (OCCAM)0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified