Metadata-Version: 2.1
Name: quick_torch
Version: 1.0.1
Summary: Library that provides a QuickDraw dataset using the Pytorch API.
Home-page: https://github.com/framunoz/quick-torch
Author: Francisco Muñoz G.
License: MIT
Classifier: Environment :: GPU :: NVIDIA CUDA :: 11.8
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: setuptools
Requires-Dist: requests
Requires-Dist: torchvision
Requires-Dist: jsonlines

# Quick, Torch!

Quick, Torch! is a simple package that provides a ["Quick, Draw!"](https://github.com/googlecreativelab/quickdraw-dataset) dataset using the abstract class `VisionDataset`, provided by `torchvision` API. This package mirrors the [MNIST](https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html) dataset provided in torchvision.

You can install this package with
```
pip install quick-torch --upgrade
```

# Example
Here are a simple example of usage:
```python
from quick_torch import QuickDraw
import torchvision.transforms as T


ds = QuickDraw(
    root="dataset", 
    categories="face", 
    download=True, 
    transform=T.Resize((128, 128))
)
print(f"{len(ds) = }")
first_data = ds[0]
first_data

>>> Downloading https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/face.npy
>>> 
>>> Downloading https://storage.googleapis.com/quickdraw_dataset/full/simplified/face.ndjson
>>> 
>>> len(ds) = 161666
>>> (<PIL.Image.Image image mode=L size=128x128>, 108)
```

For more examples, please refer to the notebook [example.ipynb](https://github.com/framunoz/quick-torch/blob/main/example.ipynb)
