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 16761700 of 10420 papers

TitleStatusHype
Instance Localization for Self-supervised Detection PretrainingCode1
GradInit: Learning to Initialize Neural Networks for Stable and Efficient TrainingCode1
AlphaNet: Improved Training of Supernets with Alpha-DivergenceCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
Cross-modal Adversarial ReprogrammingCode1
Momentum Residual Neural NetworksCode1
OntoZSL: Ontology-enhanced Zero-shot LearningCode1
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-TuningCode1
Bayesian Neural Network Priors RevisitedCode1
High-Performance Large-Scale Image Recognition Without NormalizationCode1
Training Vision Transformers for Image RetrievalCode1
BRECQ: Pushing the Limit of Post-Training Quantization by Block ReconstructionCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Negative Data AugmentationCode1
DetCo: Unsupervised Contrastive Learning for Object DetectionCode1
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural networkCode1
Open-World Semi-Supervised LearningCode1
Sill-Net: Feature Augmentation with Separated Illumination RepresentationCode1
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding AttacksCode1
Mask Guided Attention For Fine-Grained Patchy Image ClassificationCode1
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse TrainingCode1
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded PlatformsCode1
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image ClassificationCode1
PyTorch-Hebbian: facilitating local learning in a deep learning frameworkCode1
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained FeaturesCode1
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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