optical.converter.Annotation

class optical.converter.Annotation(root: str, format: str)

Bases: object

__init__(root: str, format: str)

Methods

__init__(root, format)
bbox_scatter([split, category])
bbox_stats([split, category])
describe()
export(to[, output_dir])
show_distribution()
train_test_split([test_size, stratified, ...])	splits the dataset into train and validation sets
visualizer([image_dir, split, img_size])

Attributes

label_df
splits

train_test_split(test_size: float = 0.2, stratified: bool = False, random_state: int = 42)

splits the dataset into train and validation sets

Parameters

• test_size (float, optional) – Fraction of total images to be kept for validation. Defaults to 0.2.

• stratified (bool, optional) – Whether to stratify the split. Defaults to False.

• random_state (int, optional) – random state for the split. Defaults to 42.

Returns

Returns an instance of FormatSpec class

Return type

FormatSpec