| --- |
| annotations_creators: [] |
| language: en |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - object-detection |
| task_ids: [] |
| pretty_name: Street View House Numbers |
| tags: |
| - fiftyone |
| - image |
| - object-detection |
| dataset_summary: ' |
| |
| |
| |
| |
| This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 33402 samples. |
| |
| |
| ## Installation |
| |
| |
| If you haven''t already, install FiftyOne: |
| |
| |
| ```bash |
| |
| pip install -U fiftyone |
| |
| ``` |
| |
| |
| ## Usage |
| |
| |
| ```python |
| |
| import fiftyone as fo |
| |
| import fiftyone.utils.huggingface as fouh |
| |
| |
| # Load the dataset |
| |
| # Note: other available arguments include ''max_samples'', etc |
| |
| dataset = fouh.load_from_hub("Voxel51/StreetViewHouseNumbers") |
| |
| |
| # Launch the App |
| |
| session = fo.launch_app(dataset) |
| |
| ``` |
| |
| ' |
| --- |
| |
| # 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. |
|
|
| The images are cropped to a fixed resolution of 32x32 pixels, centered around a single character but may contain some distractors at the sides. |
|
|
| SVHN is similar to the MNIST dataset but incorporates significantly more labeled data and comes from a harder, unsolved, real-world problem of recognizing digits and numbers in natural scene images. |
|
|
| The dataset here is provided as original images with character level bounding boxes |
|
|
|
|
| This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 33402 samples. |
|
|
| The recipe notebook for creating this dataset can be found [here](https://colab.research.google.com/drive/1rwlDeLbsz498nrjemaRC7Tn8IMFZw8x7?usp=sharing) |
|
|
| ## Installation |
|
|
| If you haven't already, install FiftyOne: |
|
|
| ```bash |
| pip install -U fiftyone |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| import fiftyone as fo |
| import fiftyone.utils.huggingface as fouh |
| |
| # Load the dataset |
| # Note: other available arguments include 'max_samples', etc |
| dataset = fouh.load_from_hub("Voxel51/StreetViewHouseNumbers") |
| |
| # Launch the App |
| session = fo.launch_app(dataset) |
| ``` |
|
|
|
|
| ## Dataset Details |
|
|
| - **Curated by:** Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng |
| - **Funded by:** Google Inc., Stanford University |
| - **Shared by:** [More Information Needed] |
| - **License:** non-commercial use only |
|
|
| For questions regarding the dataset, please contact streetviewhousenumbers@gmail.com |
|
|
| ### Dataset Sources |
|
|
| - **Repository:** http://ufldl.stanford.edu/housenumbers |
| - **Paper:** http://ufldl.stanford.edu/housenumbers/nips2011_housenumbers.pdf |
| |
| ## Citation |
| |
| **BibTeX:** |
| ```bibtex |
| @inproceedings{netzer2011reading, |
| title={Reading digits in natural images with unsupervised feature learning}, |
| author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y}, |
| booktitle={NIPS workshop on deep learning and unsupervised feature learning}, |
| volume={2011}, |
| number={2}, |
| pages={5}, |
| year={2011} |
| } |
| ``` |
