torchvision (version 0.7.0)

coco_detection_dataset: COCO Detection Dataset

Description

Loads the MS COCO dataset for object detection and segmentation.

Usage

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

Value

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

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

• y$boxes: a (N, 4) torch_tensor of bounding boxes in the format c(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.

• y$segmentation: a list of segmentation polygons for each object.

• y$masks: a (N, H, W) boolean torch_tensor containing binary segmentation masks.

The returned object has S3 classes "image_with_bounding_box" and "image_with_segmentation_mask"

to enable automatic dispatch by visualization functions such as draw_bounding_boxes() and draw_segmentation_masks().

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.).

Details

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

Examples

if (FALSE) {
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)

# Visualize segmentation masks (if present)
masked <- draw_segmentation_masks(item)
tensor_image_browse(masked)
}