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 551575 of 10419 papers

TitleStatusHype
Asymmetric Loss For Multi-Label ClassificationCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
AdaViT: Adaptive Tokens for Efficient Vision TransformerCode1
Astroformer: More Data Might not be all you need for ClassificationCode1
The MAMe Dataset: On the relevance of High Resolution and Variable Shape image propertiesCode1
A Survey of Classical And Quantum Sequence ModelsCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
Cross-Iteration Batch NormalizationCode1
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped WindowsCode1
A Data Set and a Convolutional Model for Iconography Classification in PaintingsCode1
Counterfactual Visual ExplanationsCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
AdaScale SGD: A User-Friendly Algorithm for Distributed TrainingCode1
Co-Tuning for Transfer LearningCode1
Counterfactual Generative NetworksCode1
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 DiagnosisCode1
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
A Single Graph Convolution Is All You Need: Efficient Grayscale Image ClassificationCode1
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
A Simple Semi-Supervised Learning Framework for Object DetectionCode1
CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare RecordsCode1
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN ExecutionCode1
Convolutional Xformers for VisionCode1
A Simple Baseline for Low-Budget Active LearningCode1
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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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified