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 54515475 of 10420 papers

TitleStatusHype
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
On the Initial Behavior Monitoring Issues in Federated Learning0
Saliency Guided Experience Packing for Replay in Continual LearningCode0
LibFewShot: A Comprehensive Library for Few-shot LearningCode2
ConvMLP: Hierarchical Convolutional MLPs for VisionCode1
Generatively Augmented Neural Network Watchdog for Image Classification Networks0
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization0
Datasets: A Community Library for Natural Language ProcessingCode3
Tom: Leveraging trend of the observed gradients for faster convergenceCode0
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)0
Knowledge Distillation Using Hierarchical Self-Supervision Augmented DistributionCode1
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification0
Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images0
Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease ClassificationCode1
Automated Robustness with Adversarial Training as a Post-Processing Step0
Cross-token Modeling with Conditional Computation0
Real-World Adversarial Examples involving Makeup Application0
Robust fine-tuning of zero-shot modelsCode1
ISyNet: Convolutional Neural Networks design for AI acceleratorCode0
Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models0
Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoostCode1
CAM-loss: Towards Learning Spatially Discriminative Feature Representations0
Ghost Loss to Question the Reliability of Training Data0
Better Self-training for Image Classification through Self-supervisionCode0
Impact of Attention on Adversarial Robustness of Image Classification Models0
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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