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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 (e.g., the images are of small cropped digits), but incorporates an order of magnitude more ...
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스탠포드 대학교에서 제공하는 실제 이미지 데이터 세트로, 기계 학습 및 객체 인식 알고리즘 개발에...
- Extra 32x32.Mat
Over 600k Real-World Images of House Numbers From Google Street View Images.
Dataset Card for Street View House Numbers. The Street View House Numbers (SVHN) dataset is a large real-world image dataset used for developing machine learning and object recognition algorithms. It contains over 600,000 labeled images of house numbers taken from Google Street View.
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.
Unlike MNIST, SVHN tackles a considerably more challenging and unsolved real-world problem—recognizing digits and numbers within natural scene images. This dataset is derived from house numbers captured in Google Street View images.
Street View House Numbers (SVHN) is a real-world dataset containing images of house numbers taken from Google's street view. This repository contains the source code needed to built machine learning algorithms that can "recognize" the numbers on the images.
This project focuses on recognizing house numbers from street-level images using deep learning techniques. The Street View House Numbers (SVHN) dataset, which includes over 600,000 labeled digits, is utilized to train and evaluate various models.
