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 826850 of 10419 papers

TitleStatusHype
Exploiting Label Skews in Federated Learning with Model ConcatenationCode1
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy LabelsCode1
Exploring Vision Transformers for Fine-grained ClassificationCode1
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile DevicesCode1
Eye-gaze Guided Multi-modal Alignment for Medical Representation LearningCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
Channel Importance Matters in Few-Shot Image ClassificationCode1
Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoostCode1
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution DetectionCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
PAD-Net: An Efficient Framework for Dynamic NetworksCode1
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax DiseasesCode1
Fast and Private Inference of Deep Neural Networks by Co-designing Activation FunctionsCode1
Adaptive and Background-Aware Vision Transformer for Real-Time UAV TrackingCode1
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
CHiLS: Zero-Shot Image Classification with Hierarchical Label SetsCode1
Automated Learning Rate Scheduler for Large-batch TrainingCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image ClassificationCode1
Advancing Vision Transformers with Group-Mix AttentionCode1
Fast Hierarchical Games for Image ExplanationsCode1
Advantages and Bottlenecks of Quantum Machine Learning for Remote SensingCode1
Class Adaptive Network CalibrationCode1
DeepViT: Towards Deeper Vision TransformerCode1
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
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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