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