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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 (over 600,000 digit images) 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.
1
, '9' has label 9
and '0' has label 10
.Please cite the following reference in papers using this dataset:
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)
Please use http://ufldl.stanford.edu/housenumbers
as the URL for this site when necessary
For questions regarding the dataset, please contact streetviewhousenumbers@gmail.com
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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, with an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real-world problem (recognizing digits and numbers in natural scene images).
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Are you sure you want to delete this access key?
Are you sure you want to delete this access key?