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SVHN

Street View House Numbers

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Street View House Numbers (SVHN) is a digit classification benchmark dataset that contains 600,000 32×32 RGB images of printed digits (from 0 to 9) cropped from pictures of house number plates. The cropped images are centered in the digit of interest, but nearby digits and other distractors are kept in the image. SVHN has three sets: training, testing sets and an extra set with 530,000 images that are less difficult and can be used for helping with the training process.

Source: Reading Digits in Natural Images with Unsupervised Feature Learning Image Source: http://ufldl.stanford.edu/housenumbers/

Benchmarks

Anomaly Detection/Mean AUC Clustering Algorithms Evaluation/Clustering Accuracy Image Classification/Percentage error Image Classification/Percentage correct Image Classification/Acc Image Classification/# of clusters (k)Image Classification/Accuracy Novel Class Discovery/Clustering Accuracy

Related Benchmarks

SVHN (1000 Labels, ImageNet-100 Unlabeled)/Image Classification/Accuracy SVHN (1000 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/Accuracy SVHN (250 Labels, ImageNet-100 Unlabeled)/Image Classification/Accuracy SVHN (250 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/Accuracy SVHN (40 Labels, ImageNet-100 Unlabeled)/Image Classification/Accuracy SVHN (40 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/Accuracy SVHN vs CIFAR-10/Out-of-Distribution Detection/AUROC SVHN vs CIFAR-100/Out-of-Distribution Detection/AUROC SVHN vs Gaussian/Out-of-Distribution Detection/AUROC SVHN vs ImageNet (C)/Out-of-Distribution Detection/AUROC SVHN vs ImageNet (R)/Out-of-Distribution Detection/AUROC SVHN vs LSUN (C)/Out-of-Distribution Detection/AUROC SVHN vs LSUN (R)/Out-of-Distribution Detection/AUROC SVHN vs Uniform/Out-of-Distribution Detection/AUROC SVHN vs iSUN/Out-of-Distribution Detection/AUROC SVHN, 1000 labels/Image Classification/Accuracy SVHN, 1000 labels/Semi-Supervised Image Classification/Accuracy SVHN, 2000 Labels/Image Classification/Accuracy SVHN, 2000 Labels/Semi-Supervised Image Classification/Accuracy SVHN, 250 Labels/Image Classification/Accuracy SVHN, 250 Labels/Semi-Supervised Image Classification/Accuracy SVHN, 40 Labels/Image Classification/Percentage error SVHN, 40 Labels/Semi-Supervised Image Classification/Percentage error SVHN, 4000 Labels/Image Classification/Accuracy SVHN, 4000 Labels/Semi-Supervised Image Classification/Accuracy SVHN, 500 Labels/Image Classification/Accuracy SVHN, 500 Labels/Semi-Supervised Image Classification/Accuracy SVHN-to-MNIST/Domain Adaptation/Accuracy

Statistics

Papers: 3,406
Benchmarks: 8

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Tasks

Anomaly Detection Clustering Algorithms Evaluation Domain Adaptation Image Classification Novel Class Discovery Semi-Supervised Image Classification Sparse Representation-based Classification Unsupervised Image Classification