Loads the MIT Places365 dataset for scene classification.
places365_dataset(
root = tempdir(),
split = c("train", "val", "test"),
transform = NULL,
target_transform = NULL,
download = FALSE,
loader = magick_loader
)
places365_dataset_large(
root = tempdir(),
split = c("train", "val", "test"),
transform = NULL,
target_transform = NULL,
download = FALSE,
loader = magick_loader
)Root directory for dataset storage. The dataset will be stored under root/<dataset-name>. Defaults to tempdir().
One of "train", "val", or "test".
Optional. A function that takes an image and returns a transformed version (e.g., normalization, cropping).
Optional. A function that transforms the label.
Logical. If TRUE, downloads the dataset to root/. If the dataset is already present, download is skipped.
A function to load an image given its path. Defaults to
magick_loader(), which uses the {magick} package.
A torch dataset of class places365_dataset. Each element is a named
list with:
x: the image as loaded (or transformed if transform is set).
y: the integer class label. For the test split, no labels are available
and y will always be NA.
The dataset provides three splits: "train", "val", and "test".
Folder structure and image layout on disk are handled internally by the loader.
This function downloads and prepares the smaller 256x256 image version (~30 GB).
For the high-resolution variant (~160 GB), use places365_dataset_large().
Note that images in the large version come in varying sizes, so resizing may be
needed before batching.
The test split corresponds to the private evaluation set used in the
Places365 challenge. Annotation files are not publicly released, so only the
images are provided.
places365_dataset_large(): High resolution variant (~160 GB).
Other classification_dataset:
caltech_dataset,
cifar10_dataset(),
eurosat_dataset(),
fer_dataset(),
fgvc_aircraft_dataset(),
flowers102_dataset(),
image_folder_dataset(),
lfw_dataset,
mnist_dataset(),
oxfordiiitpet_dataset(),
tiny_imagenet_dataset(),
whoi_plankton_dataset(),
whoi_small_coralnet_dataset()
if (FALSE) { # \dontrun{
ds <- places365_dataset(
split = "val",
download = TRUE,
transform = transform_to_tensor
)
item <- ds[1]
tensor_image_browse(item$x)
# Show class index and label
label_idx <- item$y
label_name <- ds$classes[label_idx]
cat("Label index:", label_idx, "Class name:", label_name, "\n")
dl <- dataloader(ds, batch_size = 2)
batch <- dataloader_next(dataloader_make_iter(dl))
batch$x
ds_large <- places365_dataset_large(
split = "val",
download = TRUE,
transform = . %>% transform_to_tensor() %>% transform_resize(c(256, 256))
)
dl <- torch::dataloader(dataset = ds_large, batch_size = 2)
batch <- dataloader_next(dataloader_make_iter(dl))
batch$x
} # }