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 15261550 of 10419 papers

TitleStatusHype
Boosting Convolutional Neural Networks with Middle Spectrum Grouped ConvolutionCode1
A General Regret Bound of Preconditioned Gradient Method for DNN TrainingCode1
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object NavigationCode1
Learnable Lookup Table for Neural Network QuantizationCode1
Polynomial, trigonometric, and tropical activationsCode1
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)Code1
Learn From Others and Be Yourself in Heterogeneous Federated LearningCode1
An Empirical Investigation of the Role of Pre-training in Lifelong LearningCode1
Learning Binary Semantic Embedding for Histology Image Classification and RetrievalCode1
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNetsCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Learning Filter Basis for Convolutional Neural Network CompressionCode1
Learning From Noisy Labels By Regularized Estimation Of Annotator ConfusionCode1
A deep active learning system for species identification and counting in camera trap imagesCode1
Learning Invariances in Neural NetworksCode1
Learning Multimodal Data Augmentation in Feature SpaceCode1
Boosting Multi-Label Image Classification with Complementary Parallel Self-DistillationCode1
Data Feedback Loops: Model-driven Amplification of Dataset BiasesCode1
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution EnvironmentsCode1
Learning Representations For Images With Hierarchical LabelsCode1
Boosting the Adversarial Transferability of Surrogate Models with Dark KnowledgeCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data AugmentationCode1
Learning Support and Trivial Prototypes for Interpretable Image ClassificationCode1
DARTS: Differentiable Architecture SearchCode1
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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