SVHN
OpenML dataset with id 41081
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Full work available at URL: https://api.openml.org/data/v1/download/19326077/SVHN.arff
Upload date: 4 May 2018
Copyright license: CC0
Dataset Characteristics
Number of classes: 10
Number of features: 3,073 (numeric: 3,072, symbolic: 1 and in total binary: 0 )
Number of instances: 99,289
Number of instances with missing values: 0
Number of missing values: 0
Author: Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Source: original - 2011 Please cite: Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Reading Digits in Natural Images with Unsupervised Feature Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011. PDF
The Street View House Numbers (SVHN) Dataset
SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.
It consists of 10 classes, 1 for each digit. Digit '1' has label 1, '9' has label 9 and '0' has label 10. The data comes in a MNIST-like format of 32-by-32 RGB images centered around a single digit (many of the images do contain some distractors at the sides).
This page was built for dataset: SVHN
