COCO Detection Dataset

Loads the MS COCO dataset for object detection tasks only.

coco_detection_dataset(
  root = tempdir(),
  train = TRUE,
  year = c("2017", "2014"),
  download = FALSE,
  transform = NULL,
  target_transform = NULL
)

Arguments

root

Root directory where the dataset is stored or will be downloaded to.

train

Logical. If TRUE, loads the training split; otherwise, loads the validation split.

year

Character. Dataset version year. One of "2014" or "2017".

download

Logical. If TRUE, downloads the dataset if it's not already present in the root directory.

transform

Optional transform function applied to the image.

target_transform

Optional transform function applied to the target (labels, boxes, etc.).

Value

An object of class coco_detection_dataset. Each item is a list:

• x: a (C, H, W) array representing the image.

• y$boxes: a (N, 4) torch_tensor of bounding boxes in the format \((x_{min}, y_{min}, x_{max}, y_{max})\).

• y$labels: an integer torch_tensor with the class label for each object.

• y$area: a float torch_tensor indicating the area of each object.

• y$iscrowd: a boolean torch_tensor, where TRUE marks the object as part of a crowd.

The returned object has S3 class "image_with_bounding_box" to enable automatic dispatch by visualization functions such as draw_bounding_boxes().

For instance segmentation tasks, use coco_segmentation_dataset instead.

Details

The returned image x is in CHW format (channels, height, width), matching the torch convention. The dataset y offers object detection annotations such as bounding boxes, labels, areas, and crowd indicators from the official COCO annotations.

Files are downloaded to a coco subdirectory in the torch cache directory for better organization.

See also

coco_segmentation_dataset for instance segmentation tasks

Other detection_dataset: pascal_voc_datasets, rf100_biology_collection(), rf100_damage_collection(), rf100_document_collection(), rf100_infrared_collection(), rf100_medical_collection(), rf100_underwater_collection()

Examples

if (FALSE) { # \dontrun{
# Load dataset for object detection
ds <- coco_detection_dataset(
  train = FALSE,
  year = "2017",
  download = TRUE
)

item <- ds[1]

# Visualize bounding boxes
boxed <- draw_bounding_boxes(item)
tensor_image_browse(boxed)
} # }