FGVC Aircraft Dataset
fgvc_aircraft_dataset(
root = tempdir(),
split = "train",
annotation_level = "variant",
transform = NULL,
target_transform = NULL,
download = FALSE
)Character. Root directory for dataset storage. The dataset will be stored under root/fgvc-aircraft-2013b.
Character. One of "train", "val", "trainval", or "test". Default is "train".
Character. Level of annotation to use for classification. Default is "variant".
One of "variant", "family", "manufacturer", or "all". See Details.
Optional function to transform input images after loading. Default is NULL.
Optional function to transform labels. Default is NULL.
Logical. Whether to download the dataset if not found locally. Default is FALSE.
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).
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).
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
if (FALSE) { # \dontrun{
# 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"
} # }