torchvision (version 0.8.0)

rf100_medical_collection: RoboFlow 100 Medical dataset Collection

Description

Loads one of the RoboFlow 100 Medical datasets (COCO format) with per-dataset folders and train/valid/test splits.

Usage

rf100_medical_collection(
  dataset,
  split = c("train", "test", "valid"),
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

Value

A torch dataset. Each element is a named list with:

• x: H x W x 3 array representing the image.

• y: a list containing the target with:

  • image_id: numeric identifier of the x image.

  • labels: numeric identifier of the N bounding-box object class.

  • boxes: a torch_tensor of shape (N, 4) with bounding boxes, each in \((x_{min}, y_{min}, x_{max}, y_{max})\) format.

The returned item inherits the class image_with_bounding_box so it can be visualised with helper functions such as draw_bounding_boxes().

Arguments

dataset

Dataset to select within c("radio_signal", "rheumatology", "knee", "abdomen_mri", "brain_axial_mri", "gynecology_mri", "brain_tumor", "fracture").

split

the subset of the dataset to choose between c("train", "test", "valid").

transform

Optional transform function applied to the image.

target_transform

Optional transform function applied to the target.

download

Logical. If TRUE, downloads the dataset if not present at root.

See Also

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

Examples

if (FALSE) {
ds <- rf100_medical_collection(
  dataset = "rheumatology",
  split = "test",
  transform = transform_to_tensor,
  download = TRUE
)
item <- ds[1]
boxed <- draw_bounding_boxes(item)
tensor_image_browse(boxed)
}