Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
arrow
Languages:
English
Size:
10K - 100K
Tags:
legal
License:
File size: 2,906 Bytes
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---
license: mit
task_categories:
- image-classification
language:
- en
tags:
- legal
pretty_name: The Street View House Numbers (SVHN) Dataset
size_categories:
- 10K<n<100K
dataset_info:
- config_name: default
features:
- name: image
dtype: image
- name: label
sequence:
- name: digit
dtype: uint8
- name: left
dtype: float32
- name: top
dtype: float32
- name: width
dtype: float32
- name: height
dtype: float32
splits:
- name: train
num_bytes: 7718427
num_examples: 33402
- name: test
num_bytes: 2955196
num_examples: 13068
download_size: 685311858
dataset_size: 10673623
configs:
- config_name: default
data_files:
- split: train
path: default/train/data-*.arrow
- split: test
path: default/test/data-*.arrow
---
# Dataset card for SVHN
The Street View House Numbers (SVHN) dataset is a real-world image dataset developed and designed for machine learning and object recognition algorithms, and is characterized by low data preprocessing and formatting requirements. Similar to MNIST, SVHN contains images of small cropped numbers, but in terms of labeled data, SVHN is an order of magnitude larger than MNIST, comprising over 600,000 digital images. Unlike MNIST, SVHN deals with a much more challenging and unsolved real-world problem - recognizing numbers and strings of numbers in natural scene images. The dataset is derived from house numbers captured in Google Street View images.
## Usage
### By API
```python
from datasets import load_dataset
ds = load_dataset("Genius-Society/svhn")
for item in ds["train"]:
print(item)
for item in ds["test"]:
print(item)
```
### Not By API
```python
import os
import zipfile
import requests
def download_file(url, save_path):
response = requests.get(url, stream=True)
with open(save_path, 'wb') as file:
for chunk in response.iter_content(chunk_size=1024):
if chunk:
file.write(chunk)
def unzip(zip_file_path, extract_to):
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
for member in zip_ref.infolist():
zip_ref.extract(member, extract_to)
if not os.path.exists('./data.zip'):
download_file(
'https://www.modelscope.cn/datasets/Genius-Society/svhn/resolve/master/data/svhn.zip',
'data.zip'
)
if not os.path.exists('./data'):
unzip('data.zip', './')
```
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/Genius-Society/svhn
cd svhn
```
## Mirror
<https://www.modelscope.cn/datasets/Genius-Society/svhn>
## References
[1] <a href="http://ufldl.stanford.edu/housenumbers">The Street View House Numbers (SVHN) Dataset</a><br>
[2] <https://github.com/Genius-Society/svhn_recognition> |