Table of Contents
Shortcuts

# Source code for torchvision.datasets.stanford_cars

    import pathlib
    from typing import Any, Callable, Optional, Tuple

    from PIL import Image

    from .utils import download_and_extract_archive, download_url, verify_str_arg
    from .vision import VisionDataset

    class StanfordCars(VisionDataset):
        """`Stanford Cars <https://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_ Dataset

        The Cars dataset contains 16,185 images of 196 classes of cars. The data is
        split into 8,144 training images and 8,041 testing images, where each class
        has been split roughly in a 50-50 split

        .. note::

            This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.

        Args:
            root (string): Root directory of dataset
            split (string, optional): The dataset split, supports ``"train"`` (default) or ``"test"``.
            transform (callable, optional): A function/transform that  takes in an PIL image
                and returns a transformed version. E.g, ``transforms.RandomCrop``
            target_transform (callable, optional): A function/transform that takes in the
                target and transforms it.
            download (bool, optional): If True, downloads the dataset from the internet and
                puts it in root directory. If dataset is already downloaded, it is not
                downloaded again."""

        def __init__(
            self,
            root: str,
            split: str = "train",
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
        ) -> None:

            try:
                import scipy.io as sio
            except ImportError:
                raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy")

            super().__init__(root, transform=transform, target_transform=target_transform)

            self._split = verify_str_arg(split, "split", ("train", "test"))
            self._base_folder = pathlib.Path(root) / "stanford_cars"
            devkit = self._base_folder / "devkit"

            if self._split == "train":
                self._annotations_mat_path = devkit / "cars_train_annos.mat"
                self._images_base_path = self._base_folder / "cars_train"
            else:
                self._annotations_mat_path = self._base_folder / "cars_test_annos_withlabels.mat"
                self._images_base_path = self._base_folder / "cars_test"

            if download:
                self.download()

            if not self._check_exists():
                raise RuntimeError("Dataset not found. You can use download=True to download it")

            self._samples = [
                (
                    str(self._images_base_path / annotation["fname"]),
                    annotation["class"] - 1,  # Original target mapping  starts from 1, hence -1
                )
                for annotation in sio.loadmat(self._annotations_mat_path, squeeze_me=True)["annotations"]
            ]

            self.classes = sio.loadmat(str(devkit / "cars_meta.mat"), squeeze_me=True)["class_names"].tolist()
            self.class_to_idx = {cls: i for i, cls in enumerate(self.classes)}

        def __len__(self) -> int:
            return len(self._samples)

        def __getitem__(self, idx: int) -> Tuple[Any, Any]:
            """Returns pil_image and class_id for given index"""
            image_path, target = self._samples[idx]
            pil_image = Image.open(image_path).convert("RGB")

            if self.transform is not None:
                pil_image = self.transform(pil_image)
            if self.target_transform is not None:
                target = self.target_transform(target)
            return pil_image, target

        def download(self) -> None:
            if self._check_exists():
                return

            download_and_extract_archive(
                url="https://ai.stanford.edu/~jkrause/cars/car_devkit.tgz",
                download_root=str(self._base_folder),
                md5="c3b158d763b6e2245038c8ad08e45376",
            )
            if self._split == "train":
                download_and_extract_archive(
                    url="https://ai.stanford.edu/~jkrause/car196/cars_train.tgz",
                    download_root=str(self._base_folder),
                    md5="065e5b463ae28d29e77c1b4b166cfe61",
                )
            else:
                download_and_extract_archive(
                    url="https://ai.stanford.edu/~jkrause/car196/cars_test.tgz",
                    download_root=str(self._base_folder),
                    md5="4ce7ebf6a94d07f1952d94dd34c4d501",
                )
                download_url(
                    url="https://ai.stanford.edu/~jkrause/car196/cars_test_annos_withlabels.mat",
                    root=str(self._base_folder),
                    md5="b0a2b23655a3edd16d84508592a98d10",
                )

        def _check_exists(self) -> bool:
            if not (self._base_folder / "devkit").is_dir():
                return False

            return self._annotations_mat_path.exists() and self._images_base_path.is_dir()

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.