The Street View House Numbers (SVHN) Dataset
Yuval Netzer and Tao Wang and Adam Coates and Alessandro Bissacco and Bo Wu and Andrew Y. Ng

housenumbers (3 files)
extra.tar.gz 1.96GB
test.tar.gz 276.56MB
train.tar.gz 404.14MB
Type: Dataset
Tags: deep learning

title= {The Street View House Numbers (SVHN) Dataset},
journal= {},
author= {Yuval Netzer and Tao Wang and Adam Coates and Alessandro Bissacco and Bo Wu and Andrew Y. Ng},
year= {2011},
url= {},
abstract= {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. 


10 classes, 1 for each digit. Digit '1' has label 1, '9' has label 9 and '0' has label 10.
73257 digits for training, 26032 digits for testing, and 531131 additional, somewhat less difficult samples, to use as extra training data
Comes in two formats:
1. Original images with character level bounding boxes.
2. MNIST-like 32-by-32 images centered around a single character (many of the images do contain some distractors at the sides).

These are the original, variable-resolution, color house-number images with character level bounding boxes, as shown in the examples images above. (The blue bounding boxes here are just for illustration purposes. The bounding box information are stored in digitStruct.mat instead of drawn directly on the images in the dataset.) Each tar.gz file contains the orignal images in png format, together with a digitStruct.mat file, which can be loaded using Matlab. The digitStruct.mat file contains a struct called digitStruct with the same length as the number of original images. Each element in digitStruct has the following fields: name which is a string containing the filename of the corresponding image. bbox which is a struct array that contains the position, size and label of each digit bounding box in the image. Eg: digitStruct(300).bbox(2).height gives height of the 2nd digit bounding box in the 300th image.


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.

Please use as the URL for this site when necessary 

For questions regarding the dataset, please contact

keywords= {deep learning},
terms= {}

10 day statistics (3 downloads)

Average Time 5 mins, 31 secs
Average Speed 7.95MB/s
Best Time 5 mins, 30 secs
Best Speed 7.99MB/s
Worst Time 5 mins, 35 secs
Worst Speed 7.87MB/s