pyrovision.datasets#

All datasets are subclasses of torchvision.datasets.vision.VisionDataset i.e, they have __getitem__ and __len__ methods implemented. Hence, they can all be passed to a torch.utils.data.DataLoader which can load multiple samples parallelly using torch.multiprocessing workers.

OpenFire#

An image classification dataset for wildfire in natural environments, built using Google Images referenced data.

class pyrovision.datasets.OpenFire(root: str, train: bool = True, download: bool = False, validate_images: bool = True, num_samples: Optional[int] = None, num_threads: Optional[int] = None, prefetch_fn: Optional[Callable[[Tuple[Union[Path, str], Union[Path, str]]], None]] = None, **kwargs: Any)[source]#

Implements an image classification dataset for wildfire detection, collected from web searches.

>>> from pyrovision.datasets import OpenFire
>>> train_set = OpenFire("path/to/your/folder", train=True, download=True)
>>> img, target = train_set[0]
Parameters:

  • root – Root directory where ‘OpenFire’ is located.

  • train – If True, returns training subset, else validation set.

  • download – If True, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

  • num_samples – Number of samples to download (all by default)

  • num_threads – If download is set to True, use this amount of threads for downloading the dataset.

  • prefetch_fn – optional function that will be applied to all images before data loading

  • **kwargs – optional arguments of torchvision.datasets.VisionDataset