randall-lab/svhn

SVHN是一个用于开发机器学习和对象识别算法的现实世界图像数据集，对数据预处理和格式化的要求最低。它可以看作是与MNIST类似的数据集（例如，图像是小的裁剪数字），但是包含的标记数据量是一个数量级以上（超过60万张数字图像），并且来源于一个更难、未解决的、现实世界的问题（在自然场景图像中识别数字和号码）。SVHN是从Google街景图像中的房屋号码获得的。

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.