luxonis_ml.data.loaders

Basic Usage

from luxonis_ml.data import LuxonisDataset, LuxonisLoader

dataset = LuxonisDataset("parking_lot")
loader = LuxonisLoader(dataset, view="train")

inputs, labels = loader[0]

LuxonisLoader implements indexed access and iteration. The returned value is always (inputs, labels).

For single-source datasets, inputs is a single image-like array. For multi-source datasets, inputs is a dictionary mapping source names to arrays.

Constructor Options

LuxonisLoader is configured at runtime and does not mutate stored dataset state. Common options include:

• view to load one split or a list of splits.
• augmentation_engine and augmentation_config to enable augmentations.
• height, width, and keep_aspect_ratio to define the resize behavior expected by the augmentation engine.
• color_space to request "RGB", "BGR", or "GRAY" output globally or per source name.
• seed for reproducible random augmentations.
• exclude_empty_annotations to omit empty labels.
• keep_categorical_as_strings to preserve categorical metadata values.
• update_mode to control media synchronization for remote datasets.
• filter_task_names to load only selected task groups.

When a remote dataset is loaded, annotations and metadata are refreshed. Media files are downloaded according to UpdateMode: ALL overwrites local media and MISSING downloads only media that cannot be resolved locally.

Label Keys

The labels dictionary is keyed by "task_name/task_type". If a dataset was created without a task name, the default task name is the empty string and keys start with "/".

>>> task_name = "detection"
>>> task_type = "boundingbox"
>>> f"{task_name}/{task_type}"
'detection/boundingbox'
>>> f"{''}/metadata/camera_angle"
'/metadata/camera_angle'

Metadata labels use "task_name/metadata/key" so each metadata field can be consumed independently.

Output Layouts

Runtime Options

LuxonisLoader owns runtime concerns that are intentionally separate from dataset storage:

• selected views through view;
• color-space conversion through color_space;
• optional resizing through height and width;
• aspect-ratio preservation through keep_aspect_ratio;
• augmentation engine construction through augmentation_engine and augmentation_config;
• remote media synchronization through update_mode;
• empty-annotation filtering through exclude_empty_annotations;
• metadata category encoding through keep_categorical_as_strings;
• task filtering through filter_task_names.

loader = LuxonisLoader(
    dataset,
    view=["train", "val"],
    height=640,
    width=640,
    keep_aspect_ratio=True,
    color_space="RGB",
    filter_task_names=["detection"],
    exclude_empty_annotations=True,
)