RoboFlow 100 Document dataset Collection
rf100_document_collection(
dataset,
split = c("train", "test", "valid"),
transform = NULL,
target_transform = NULL,
download = FALSE
)Dataset to select within c("tweeter_post", "tweeter_profile", "document_part",
"activity_diagram", "signature", "paper_part", "tabular_data", "paragraph").
the subset of the dataset to choose between c("train", "test", "valid").
Optional transform function applied to the image.
Optional transform function applied to the target.
Logical. If TRUE, downloads the dataset if not present at root.
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().
Loads one of the RoboFlow 100 Document datasets with COCO-style bounding box annotations for object detection tasks.
Other detection_dataset:
coco_detection_dataset(),
pascal_voc_datasets,
rf100_biology_collection(),
rf100_damage_collection(),
rf100_infrared_collection(),
rf100_medical_collection(),
rf100_underwater_collection()
if (FALSE) { # \dontrun{
ds <- rf100_document_collection(
dataset = "tweeter_post",
split = "train",
transform = transform_to_tensor,
download = TRUE
)
# Retrieve a sample and inspect annotations
item <- ds[1]
item$y$labels
item$y$boxes
# Draw bounding boxes and display the image
boxed_img <- draw_bounding_boxes(item)
tensor_image_browse(boxed_img)
} # }