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 20012050 of 10419 papers

TitleStatusHype
UniformAugment: A Search-free Probabilistic Data Augmentation ApproachCode1
Designing Network Design SpacesCode1
TResNet: High Performance GPU-Dedicated ArchitectureCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNetsCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
GAN-based Priors for Quantifying UncertaintyCode1
Convolutional Spiking Neural Networks for Spatio-Temporal Feature ExtractionCode1
Hit-Detector: Hierarchical Trinity Architecture Search for Object DetectionCode1
Circumventing Outliers of AutoAugment with Knowledge DistillationCode1
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?Code1
Meta Pseudo LabelsCode1
Adversarial Continual LearningCode1
Adversarial Robustness on In- and Out-Distribution Improves ExplainabilityCode1
Overinterpretation reveals image classification model pathologiesCode1
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial LocationCode1
Synthesizing human-like sketches from natural images using a conditional convolutional decoderCode1
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot LearningCode1
SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image ClassificationCode1
Improved Baselines with Momentum Contrastive LearningCode1
Π-nets: Deep Polynomial Neural NetworksCode1
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
TaskNorm: Rethinking Batch Normalization for Meta-LearningCode1
SimLoss: Class Similarities in Cross EntropyCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
AIDeveloper: deep learning image classification in life science and beyondCode1
Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)Code1
Denoised Smoothing: A Provable Defense for Pretrained ClassifiersCode1
Curriculum By SmoothingCode1
Out-of-Distribution Generalization via Risk Extrapolation (REx)Code1
FMix: Enhancing Mixed Sample Data AugmentationCode1
RNNPool: Efficient Non-linear Pooling for RAM Constrained InferenceCode1
Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation MappingCode1
A Comprehensive Approach to Unsupervised Embedding Learning based on AND AlgorithmCode1
On Feature Normalization and Data AugmentationCode1
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers AdaptivelyCode1
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural NetworksCode1
Scheduled Restart Momentum for Accelerated Stochastic Gradient DescentCode1
Stochasticity in Neural ODEs: An Empirical StudyCode1
A Toolkit for Generating Code Knowledge GraphsCode1
Towards Robust and Reproducible Active Learning Using Neural NetworksCode1
KaoKore: A Pre-modern Japanese Art Facial Expression DatasetCode1
Algorithm-hardware Co-design for Deformable ConvolutionCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid CellsCode1
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance LearningCode1
Superpixel Image Classification with Graph Attention NetworksCode1
Scalable and Practical Natural Gradient for Large-Scale Deep LearningCode1
BionoiNet: ligand-binding site classification with off-the-shelf deep neural networkCode1
Cross-Iteration Batch NormalizationCode1
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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