torchvision (version 0.7.0)

fgvc_aircraft_dataset: FGVC Aircraft Dataset

Description

The FGVC-Aircraft dataset supports the following official splits:

• "train": training subset with labels.

• "val": validation subset with labels.

• "trainval": combined training and validation set with labels.

• "test": test set with labels (used for evaluation).

Usage

fgvc_aircraft_dataset(
  root = tempdir(),
  split = "train",
  annotation_level = "variant",
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

Value

An object of class fgvc_aircraft_dataset, which behaves like a torch-style dataset. Each element is a named list with:

• x: an array of shape (H, W, C) with pixel values in the range (0, 255). Please note that images have varying sizes.

• y: for single-level annotation ("variant", "family", "manufacturer"): an integer class label. for multi-level annotation ("all"): a vector of three integers c(manufacturer_idx, family_idx, variant_idx).

Arguments

root

Character. Root directory for dataset storage. The dataset will be stored under root/fgvc-aircraft-2013b.

split

Character. One of "train", "val", "trainval", or "test". Default is "train".

annotation_level

Character. Level of annotation to use for classification. Default is "variant". One of "variant", "family", "manufacturer", or "all". See Details.

transform

Optional function to transform input images after loading. Default is NULL.

target_transform

Optional function to transform labels. Default is NULL.

download

Logical. Whether to download the dataset if not found locally. Default is FALSE.

Details

The annotation_level determines the granularity of labels used for classification and supports four values:

• "variant": the most fine-grained level, e.g., "Boeing 737-700". There are 100 visually distinguishable variants.

• "family": a mid-level grouping, e.g., "Boeing 737", which includes multiple variants. There are 70 distinct families.

• "manufacturer": the coarsest level, e.g., "Boeing", grouping multiple families under a single manufacturer. There are 30 manufacturers.

• "all": multi-label format that returns all three levels as a vector of class indices c(manufacturer_idx, family_idx, variant_idx).

These levels form a strict hierarchy: each "manufacturer" consists of multiple "families", and each "family" contains several "variants". Not all combinations of levels are valid — for example, a "variant" always belongs to exactly one "family", and a "family" to exactly one "manufacturer".

When annotation_level = "all" is used, the $classes field is a named list with three components:

• classes$manufacturer: a character vector of manufacturer names

• classes$family: a character vector of family names

• classes$variant: a character vector of variant names

See Also

Other classification_dataset: caltech_dataset, cifar10_dataset(), eurosat_dataset(), fer_dataset(), flowers102_dataset(), mnist_dataset(), oxfordiiitpet_dataset(), tiny_imagenet_dataset()

Examples

if (FALSE) {
# Single-label classification
fgvc <- fgvc_aircraft_dataset(transform = transform_to_tensor, download = TRUE)

# Create a custom collate function to resize images and prepare batches
resize_collate_fn <- function(batch) {
  xs <- lapply(batch, function(item) {
    torchvision::transform_resize(item$x, c(768, 1024))
  })
  xs <- torch::torch_stack(xs)
  ys <- torch::torch_tensor(sapply(batch, function(item) item$y), dtype = torch::torch_long())
  list(x = xs, y = ys)
}
dl <- torch::dataloader(dataset = fgvc, batch_size = 2, collate_fn = resize_collate_fn)
batch <- dataloader_next(dataloader_make_iter(dl))
batch$x  # batched image tensors with shape (2, 3, 768, 1024)
batch$y  # class labels as integer tensor of shape 2

# Multi-label classification
fgvc <- fgvc_aircraft_dataset(split = "test", annotation_level = "all")
item <- fgvc[1]
item$x  # a double vector representing the image
item$y  # an integer vector of length 3: manufacturer, family, and variant indices
fgvc$classes$manufacturer[item$y[1]]  # e.g., "Boeing"
fgvc$classes$family[item$y[2]]        # e.g., "Boeing 707"
fgvc$classes$variant[item$y[3]]       # e.g., "707-320"
}