Dataset

Dataset

LightlyStudio Dataset.

Dataset

Dataset(collection: CollectionTable)

Bases: Generic[T], ABC

A LightlyStudio Dataset, a generic base for all dataset classes.

collection_id property

collection_id: UUID

Get the collection ID.

dataset_id property

dataset_id: UUID

Get the dataset ID.

name property

name: str

Get the dataset name.

__getitem__

__getitem__(key: _SliceType) -> DatasetQuery[T]

Create a query on the dataset and enable bracket notation for slicing.

Parameters:

Name	Type	Description	Default
key	_SliceType	A slice object (e.g., [10:20], [:50], [100:]).	required

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery with slice applied.

Raises:

Type	Description
TypeError	If key is not a slice object.
ValueError	If slice contains unsupported features or conflicts with existing slice.

__iter__

__iter__() -> Iterator[T]

Iterate over samples in the dataset.

compute_similarity_metadata

compute_similarity_metadata(
    query_tag_name: str, embedding_model_name: str | None = None, metadata_name: str | None = None
) -> str

Computes similarity with respect to a query tag.

Parameters:

Name	Type	Description	Default
query_tag_name	str	The name of the tag to use for the query.	required
embedding_model_name	str | None	The name of the embedding model to use. If not given, the default embedding model is used.	None
metadata_name	str | None	The name of the metadata to store the similarity values in. If not given, a name is generated automatically.	None

Returns:

Type	Description
str	The name of the metadata storing the similarity values.

compute_typicality_metadata

compute_typicality_metadata(
    embedding_model_name: str | None = None, metadata_name: str = "typicality"
) -> None

Computes typicality from embeddings, for K nearest neighbors.

Parameters:

Name	Type	Description	Default
embedding_model_name	str | None	The name of the embedding model to use. If not given, the default embedding model is used.	None
metadata_name	str	The name of the metadata to store the typicality values in. If not give, the default name "typicality" is used.	'typicality'

get_sample abstractmethod

get_sample(sample_id: UUID) -> T

Get a single sample from the dataset by its ID.

match

match(match_expression: MatchExpression) -> DatasetQuery[T]

Create a query on the dataset and store a field condition for filtering.

Parameters:

Name	Type	Description	Default
match_expression	MatchExpression	Defines the filter.	required

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery for method chaining.

order_by

order_by(*order_by: OrderByExpression) -> DatasetQuery[T]

Create a query on the dataset and store ordering expressions.

Parameters:

Name	Type	Description	Default
order_by	OrderByExpression	One or more ordering expressions. They are applied in order. E.g. first ordering by sample width and then by sample file_name will only order the samples with the same sample width by file_name.	()

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery for method chaining.

query

query() -> DatasetQuery[T]

Create a DatasetQuery for this dataset.

Returns:

Type	Description
DatasetQuery[T]	A DatasetQuery instance for querying samples in this dataset.

sample_class abstractmethod staticmethod

sample_class() -> type[T]

Returns the sample class type.

sample_type abstractmethod staticmethod

sample_type() -> SampleType

Returns the sample type.

slice

slice(offset: int = 0, limit: int | None = None) -> DatasetQuery[T]

Create a query on the dataset and apply offset and limit to results.

Parameters:

Name	Type	Description	Default
offset	int	Number of items to skip from beginning (default: 0).	0
limit	int | None	Maximum number of items to return (None = no limit).	None

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery for method chaining.

update_metadata

update_metadata(sample_metadata: list[tuple[UUID, Mapping[str, Any]]]) -> None

Bulk update metadata for multiple samples in the dataset.

If a sample does not have metadata, a new metadata row is created. If a sample already has metadata, the new key-value pairs are merged with the existing metadata.

Note: we do not check for performance reasons if the sample IDs actually belong to this dataset.

Parameters:

Name	Type	Description	Default
sample_metadata	list[tuple[UUID, Mapping[str, Any]]]	List of (sample ID, metadata_map) tuples, where metadata_map is a mapping from string to any type, for example {"weather": "cloudy", "temperature": 25}.	required

Example

dataset.update_metadata([
    (UUID("..."), {"weather": "sunny"}),
    (UUID("..."), {"weather": "cloudy", "temperature": 25}),
])

ImageDataset

ImageDataset

ImageDataset(collection: CollectionTable)

Bases: BaseSampleDataset[ImageSample]

Image dataset.

It can be created or loaded using one of the static methods:

dataset = ImageDataset.create()
dataset = ImageDataset.load()
dataset = ImageDataset.load_or_create()

Samples can be added to the dataset using various methods:

dataset.add_images_from_path(...)
dataset.add_samples_from_yolo(...)
dataset.add_samples_from_coco(...)
dataset.add_samples_from_coco_caption(...)
dataset.add_samples_from_labelformat(...)

The dataset samples can be queried directly by iterating over it or slicing it:

dataset = ImageDataset.load("my_dataset")
first_ten_samples = dataset[:10]
for sample in dataset:
    print(sample.file_name)
    sample.metadata["new_key"] = "new_value"

For filtering or ordering samples first, use the query interface:

from lightly_studio.core.dataset_query.image_sample_field import ImageSampleField

dataset = ImageDataset.load("my_dataset")
query = dataset.match(ImageSampleField.width > 10).order_by(ImageSampleField.file_name)
for sample in query:
    ...

collection_id property

collection_id: UUID

Get the collection ID.

dataset_id property

dataset_id: UUID

Get the dataset ID.

name property

name: str

Get the dataset name.

__getitem__

__getitem__(key: _SliceType) -> DatasetQuery[T]

Create a query on the dataset and enable bracket notation for slicing.

Parameters:

Name	Type	Description	Default
key	_SliceType	A slice object (e.g., [10:20], [:50], [100:]).	required

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery with slice applied.

Raises:

Type	Description
TypeError	If key is not a slice object.
ValueError	If slice contains unsupported features or conflicts with existing slice.

__iter__

__iter__() -> Iterator[T]

Iterate over samples in the dataset.

add_images_from_path

add_images_from_path(
    path: PathLike,
    allowed_extensions: Iterable[str] | None = None,
    embed: bool = True,
    tag_depth: int = 0,
) -> None

Adding images from the specified path to the dataset.

Parameters:

Name	Type	Description	Default
path	PathLike	Path to the folder containing the images to add.	required
allowed_extensions	Iterable[str] | None	An iterable container of allowed image file extensions.	None
embed	bool	If True, generate embeddings for the newly added images.	True
tag_depth	int	Defines the tagging behavior based on directory depth. - tag_depth=0 (default): No automatic tagging is performed. - tag_depth=1: Automatically creates a tag for each image based on its parent directory's name.	0

Raises:

Type	Description
NotImplementedError	If tag_depth > 1.

add_samples_from_coco

add_samples_from_coco(
    annotations_json: PathLike,
    images_path: PathLike,
    annotation_type: AnnotationType = OBJECT_DETECTION,
    split: str | None = None,
    embed: bool = True,
) -> None

Load a dataset in COCO Object Detection format and store in DB.

Parameters:

Name	Type	Description	Default
annotations_json	PathLike	Path to the COCO annotations JSON file.	required
images_path	PathLike	Path to the folder containing the images.	required
annotation_type	AnnotationType	The type of annotation to be loaded (e.g., 'ObjectDetection', 'InstanceSegmentation').	OBJECT_DETECTION
split	str | None	Optional split name to tag samples (e.g., 'train', 'val'). If provided, all samples will be tagged with this name.	None
embed	bool	If True, generate embeddings for the newly added samples.	True

add_samples_from_coco_caption

add_samples_from_coco_caption(
    annotations_json: PathLike, images_path: PathLike, split: str | None = None, embed: bool = True
) -> None

Load a dataset in COCO caption format and store in DB.

Parameters:

Name	Type	Description	Default
annotations_json	PathLike	Path to the COCO caption JSON file.	required
images_path	PathLike	Path to the folder containing the images.	required
split	str | None	Optional split name to tag samples (e.g., 'train', 'val'). If provided, all samples will be tagged with this name.	None
embed	bool	If True, generate embeddings for the newly added samples.	True

add_samples_from_labelformat

add_samples_from_labelformat(
    input_labels: ObjectDetectionInput | InstanceSegmentationInput,
    images_path: PathLike,
    split: str | None = None,
    embed: bool = True,
) -> None

Load a dataset from a labelformat object and store in database.

Parameters:

Name	Type	Description	Default
input_labels	ObjectDetectionInput | InstanceSegmentationInput	The labelformat input object.	required
images_path	PathLike	Path to the folder containing the images.	required
split	str | None	Optional split name to tag samples (e.g., 'train', 'val'). If provided, all samples will be tagged with this name.	None
embed	bool	If True, generate embeddings for the newly added samples.	True

add_samples_from_lightly

add_samples_from_lightly(
    input_folder: PathLike,
    images_rel_path: str = "../images",
    split: str | None = None,
    embed: bool = True,
) -> None

Load a dataset in Lightly format and store in DB.

Parameters:

Name	Type	Description	Default
input_folder	PathLike	Path to the folder containing the annotations/predictions.	required
images_rel_path	str	Relative path to images folder from label folder.	'../images'
split	str | None	Optional split name to tag samples (e.g., 'train', 'val'). If provided, all samples will be tagged with this name.	None
embed	bool	If True, generate embeddings for the newly added samples.	True

add_samples_from_pascal_voc_segmentations

add_samples_from_pascal_voc_segmentations(
    images_path: PathLike,
    masks_path: PathLike,
    class_id_to_name: Mapping[int, str],
    split: str | None = None,
    embed: bool = True,
) -> None

Load a Pascal VOC segmentation dataset and store in DB.

Pascal VOC masks encode class IDs per pixel (semantic segmentation). Imported masks are persisted as AnnotationType.INSTANCE_SEGMENTATION. Query and export workflows should use instance segmentation type filters.

Parameters:

Name	Type	Description	Default
images_path	PathLike	Path to the folder containing the images.	required
masks_path	PathLike	Path to the folder containing the masks.	required
class_id_to_name	Mapping[int, str]	Mapping from class IDs to class names.	required
split	str | None	Optional split name to tag samples (e.g., 'train', 'val'). If provided, all samples will be tagged with this name.	None
embed	bool	If True, generate embeddings for the newly added samples.	True

add_samples_from_yolo

add_samples_from_yolo(
    data_yaml: PathLike, input_split: str | None = None, embed: bool = True
) -> None

Load a dataset in YOLO format and store in DB.

Parameters:

Name	Type	Description	Default
data_yaml	PathLike	Path to the YOLO data.yaml file.	required
input_split	str | None	The split to load (e.g., 'train', 'val', 'test'). If None, all available splits will be loaded and assigned a corresponding tag.	None
embed	bool	If True, generate embeddings for the newly added samples.	True

compute_similarity_metadata

compute_similarity_metadata(
    query_tag_name: str, embedding_model_name: str | None = None, metadata_name: str | None = None
) -> str

Computes similarity with respect to a query tag.

Parameters:

Name	Type	Description	Default
query_tag_name	str	The name of the tag to use for the query.	required
embedding_model_name	str | None	The name of the embedding model to use. If not given, the default embedding model is used.	None
metadata_name	str | None	The name of the metadata to store the similarity values in. If not given, a name is generated automatically.	None

Returns:

Type	Description
str	The name of the metadata storing the similarity values.

compute_typicality_metadata

compute_typicality_metadata(
    embedding_model_name: str | None = None, metadata_name: str = "typicality"
) -> None

Computes typicality from embeddings, for K nearest neighbors.

Parameters:

Name	Type	Description	Default
embedding_model_name	str | None	The name of the embedding model to use. If not given, the default embedding model is used.	None
metadata_name	str	The name of the metadata to store the typicality values in. If not give, the default name "typicality" is used.	'typicality'

create classmethod

create(name: str | None = None) -> Self

Create a new dataset.

Parameters:

Name	Type	Description	Default
name	str | None	The name of the dataset. If None, a default name is used.	None

export

export(query: DatasetQuery | None = None) -> ImageDatasetExport

Return an ImageDatasetExport instance which can export the dataset in various formats.

Parameters:

Name	Type	Description	Default
query	DatasetQuery | None	The dataset query to export. If None, the default query self.query() is used.	None

get_sample

get_sample(sample_id: UUID) -> ImageSample

Get a single sample from the dataset by its ID.

Parameters:

Name	Type	Description	Default
sample_id	UUID	The UUID of the sample to retrieve.	required

Returns:

Type	Description
ImageSample	A single ImageSample object.

Raises:

Type	Description
IndexError	If no sample is found with the given sample_id.

load classmethod

load(name: str | None = None) -> Self

Load an existing dataset.

load_or_create classmethod

load_or_create(name: str | None = None) -> Self

Create a new image dataset or load an existing one.

Parameters:

Name	Type	Description	Default
name	str | None	The name of the dataset. If None, a default name is used.	None

match

match(match_expression: MatchExpression) -> DatasetQuery[T]

Create a query on the dataset and store a field condition for filtering.

Parameters:

Name	Type	Description	Default
match_expression	MatchExpression	Defines the filter.	required

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery for method chaining.

order_by

order_by(*order_by: OrderByExpression) -> DatasetQuery[T]

Create a query on the dataset and store ordering expressions.

Parameters:

Name	Type	Description	Default
order_by	OrderByExpression	One or more ordering expressions. They are applied in order. E.g. first ordering by sample width and then by sample file_name will only order the samples with the same sample width by file_name.	()

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery for method chaining.

query

query() -> DatasetQuery[T]

Create a DatasetQuery for this dataset.

Returns:

Type	Description
DatasetQuery[T]	A DatasetQuery instance for querying samples in this dataset.

sample_class staticmethod

sample_class() -> type[ImageSample]

Returns the sample class.

sample_type staticmethod

sample_type() -> SampleType

Returns the sample type.

slice

slice(offset: int = 0, limit: int | None = None) -> DatasetQuery[T]

Create a query on the dataset and apply offset and limit to results.

Parameters:

Name	Type	Description	Default
offset	int	Number of items to skip from beginning (default: 0).	0
limit	int | None	Maximum number of items to return (None = no limit).	None

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery for method chaining.

update_metadata

update_metadata(sample_metadata: list[tuple[UUID, Mapping[str, Any]]]) -> None

Bulk update metadata for multiple samples in the dataset.

If a sample does not have metadata, a new metadata row is created. If a sample already has metadata, the new key-value pairs are merged with the existing metadata.

Note: we do not check for performance reasons if the sample IDs actually belong to this dataset.

Parameters:

Name	Type	Description	Default
sample_metadata	list[tuple[UUID, Mapping[str, Any]]]	List of (sample ID, metadata_map) tuples, where metadata_map is a mapping from string to any type, for example {"weather": "cloudy", "temperature": 25}.	required

Example

dataset.update_metadata([
    (UUID("..."), {"weather": "sunny"}),
    (UUID("..."), {"weather": "cloudy", "temperature": 25}),
])

VideoDataset

VideoDataset

VideoDataset(collection: CollectionTable)

Bases: BaseSampleDataset[VideoSample]

Video dataset.

It can be created or loaded using one of the static methods:

dataset = VideoDataset.create()
dataset = VideoDataset.load()
dataset = VideoDataset.load_or_create()

Samples can be added to the dataset using:

dataset.add_videos_from_path(...)

The dataset samples can be queried directly by iterating over it or slicing it:

dataset = VideoDataset.load("my_dataset")
first_ten_samples = dataset[:10]
for sample in dataset:
    print(sample.file_name)
    sample.metadata["new_key"] = "new_value"

For filtering or ordering samples first, use the query interface:

from lightly_studio.core.dataset_query.video_sample_field import VideoSampleField

dataset = VideoDataset.load("my_dataset")
query = dataset.match(VideoSampleField.width > 10).order_by(VideoSampleField.file_name)
for sample in query:
    ...

collection_id property

collection_id: UUID

Get the collection ID.

dataset_id property

dataset_id: UUID

Get the dataset ID.

name property

name: str

Get the dataset name.

__getitem__

__getitem__(key: _SliceType) -> DatasetQuery[T]

Create a query on the dataset and enable bracket notation for slicing.

Parameters:

Name	Type	Description	Default
key	_SliceType	A slice object (e.g., [10:20], [:50], [100:]).	required

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery with slice applied.

Raises:

Type	Description
TypeError	If key is not a slice object.
ValueError	If slice contains unsupported features or conflicts with existing slice.

__iter__

__iter__() -> Iterator[T]

Iterate over samples in the dataset.

add_videos_from_path

add_videos_from_path(
    path: PathLike,
    allowed_extensions: Iterable[str] | None = None,
    num_decode_threads: int | None = None,
    embed: bool = True,
) -> None

Adding video frames from the specified path to the dataset.

Parameters:

Name	Type	Description	Default
path	PathLike	Path to the folder containing the videos to add.	required
allowed_extensions	Iterable[str] | None	An iterable container of allowed video file extensions in lowercase, including the leading dot. If None, uses default VIDEO_EXTENSIONS.	None
num_decode_threads	int | None	Optional override for the number of FFmpeg decode threads. If omitted, the available CPU cores - 1 (max 16) are used.	None
embed	bool	If True, generate embeddings for the newly added videos.	True

add_videos_from_youtube_vis

add_videos_from_youtube_vis(
    annotations_json: PathLike,
    videos_path: PathLike,
    allowed_extensions: Iterable[str] | None = None,
    annotation_type: AnnotationType = OBJECT_DETECTION,
    embed: bool = True,
) -> None

Load videos and YouTube-VIS annotations and store them in the database.

Parameters:

Name	Type	Description	Default
annotations_json	PathLike	Path to the YouTube-VIS annotations JSON file.	required
videos_path	PathLike	Path to the folder containing the videos.	required
allowed_extensions	Iterable[str] | None	An iterable container of allowed video file extensions in lowercase, including the leading dot. If None, uses default VIDEO_EXTENSIONS. Note: This is used when a path in YouTube-VIS does not contain the file extension.	None
annotation_type	AnnotationType	The type of annotation to be loaded (e.g., 'ObjectDetection', 'InstanceSegmentation').	OBJECT_DETECTION
embed	bool	If True, generate embeddings for the newly added videos.	True

compute_similarity_metadata

compute_similarity_metadata(
    query_tag_name: str, embedding_model_name: str | None = None, metadata_name: str | None = None
) -> str

Computes similarity with respect to a query tag.

Parameters:

Name	Type	Description	Default
query_tag_name	str	The name of the tag to use for the query.	required
embedding_model_name	str | None	The name of the embedding model to use. If not given, the default embedding model is used.	None
metadata_name	str | None	The name of the metadata to store the similarity values in. If not given, a name is generated automatically.	None

Returns:

Type	Description
str	The name of the metadata storing the similarity values.

compute_typicality_metadata

compute_typicality_metadata(
    embedding_model_name: str | None = None, metadata_name: str = "typicality"
) -> None

Computes typicality from embeddings, for K nearest neighbors.

Parameters:

Name	Type	Description	Default
embedding_model_name	str | None	The name of the embedding model to use. If not given, the default embedding model is used.	None
metadata_name	str	The name of the metadata to store the typicality values in. If not give, the default name "typicality" is used.	'typicality'

create classmethod

create(name: str | None = None) -> Self

Create a new dataset.

Parameters:

Name	Type	Description	Default
name	str | None	The name of the dataset. If None, a default name is used.	None

export

export(query: DatasetQuery[VideoSample] | None = None) -> VideoDatasetExport

Return an export interface for the (optionally filtered) video dataset.

get_sample

get_sample(sample_id: UUID) -> VideoSample

Get a single sample from the dataset by its ID.

Parameters:

Name	Type	Description	Default
sample_id	UUID	The UUID of the sample to retrieve.	required

Returns:

Type	Description
VideoSample	A single VideoSample object.

Raises:

Type	Description
IndexError	If no sample is found with the given sample_id.

load classmethod

load(name: str | None = None) -> Self

Load an existing dataset.

load_or_create classmethod

load_or_create(name: str | None = None) -> Self

Create a new image dataset or load an existing one.

Parameters:

Name	Type	Description	Default
name	str | None	The name of the dataset. If None, a default name is used.	None

match

match(match_expression: MatchExpression) -> DatasetQuery[T]

Create a query on the dataset and store a field condition for filtering.

Parameters:

Name	Type	Description	Default
match_expression	MatchExpression	Defines the filter.	required

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery for method chaining.

order_by

order_by(*order_by: OrderByExpression) -> DatasetQuery[T]

Create a query on the dataset and store ordering expressions.

Parameters:

Name	Type	Description	Default
order_by	OrderByExpression	One or more ordering expressions. They are applied in order. E.g. first ordering by sample width and then by sample file_name will only order the samples with the same sample width by file_name.	()

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery for method chaining.

query

query() -> DatasetQuery[T]

Create a DatasetQuery for this dataset.

Returns:

Type	Description
DatasetQuery[T]	A DatasetQuery instance for querying samples in this dataset.

sample_class staticmethod

sample_class() -> type[VideoSample]

Returns the sample class.

sample_type staticmethod

sample_type() -> SampleType

Returns the sample type.

slice

slice(offset: int = 0, limit: int | None = None) -> DatasetQuery[T]

Create a query on the dataset and apply offset and limit to results.

Parameters:

Name	Type	Description	Default
offset	int	Number of items to skip from beginning (default: 0).	0
limit	int | None	Maximum number of items to return (None = no limit).	None

Returns:

Type	Description
DatasetQuery[T]	DatasetQuery for method chaining.

update_metadata

update_metadata(sample_metadata: list[tuple[UUID, Mapping[str, Any]]]) -> None

Bulk update metadata for multiple samples in the dataset.

If a sample does not have metadata, a new metadata row is created. If a sample already has metadata, the new key-value pairs are merged with the existing metadata.

Note: we do not check for performance reasons if the sample IDs actually belong to this dataset.

Parameters:

Name	Type	Description	Default
sample_metadata	list[tuple[UUID, Mapping[str, Any]]]	List of (sample ID, metadata_map) tuples, where metadata_map is a mapping from string to any type, for example {"weather": "cloudy", "temperature": 25}.	required

Example

dataset.update_metadata([
    (UUID("..."), {"weather": "sunny"}),
    (UUID("..."), {"weather": "cloudy", "temperature": 25}),
])

ImageDatasetExport

Exports datasets from Lightly Studio into various formats.

ImageDatasetExport

ImageDatasetExport(session: Session, dataset_id: UUID, samples: Iterable[ImageSample])

Provides methods to export a dataset or a subset of it.

This class is typically not instantiated directly but returned by Dataset.export(). It allows exporting data in various formats.

Parameters:

Name	Type	Description	Default
session	Session	The database session.	required
dataset_id	UUID	The dataset ID for label retrieval.	required
samples	Iterable[ImageSample]	Samples to export.	required

to_coco_captions

to_coco_captions(output_json: PathLike | None = None) -> None

Exports captions to a COCO format JSON file.

Parameters:

Name	Type	Description	Default
output_json	PathLike | None	The path to the output COCO JSON file. If not provided, defaults to "coco_export.json" in the current working directory.	None

to_coco_instance_segmentations

to_coco_instance_segmentations(output_json: PathLike | None = None) -> None

Exports instance segmentations to a COCO format JSON file.

Parameters:

Name	Type	Description	Default
output_json	PathLike | None	The path to the output COCO JSON file. If not provided, defaults to "coco_export.json" in the current working directory.	None

to_coco_object_detections

to_coco_object_detections(output_json: PathLike | None = None) -> None

Exports object detection annotations to a COCO format JSON file.

Parameters:

Name	Type	Description	Default
output_json	PathLike | None	The path to the output COCO JSON file. If not provided, defaults to "coco_export.json" in the current working directory.	None

Raises:

Type	Description
ValueError	If the annotation task with the given name does not exist.

to_pascalvoc_instance_segmentation

to_pascalvoc_instance_segmentation(output_folder: PathLike) -> None

Exports instance segmentation annotations to Pascal VOC format.

Creates a folder with per-pixel class masks (PNG) and a class map (JSON).

Parameters:

Name	Type	Description	Default
output_folder	PathLike	The folder where Pascal VOC segmentation files are written. The folder contains a SegmentationClass subfolder with PNG masks and a class_id_to_name.json file.	required

VideoDatasetExport

Exports video datasets from Lightly Studio into various formats.

VideoDatasetExport

VideoDatasetExport(session: Session, samples: Iterable[VideoSample])

Provides methods to export a video dataset or a subset of it.

to_youtube_vis_instance_segmentation

to_youtube_vis_instance_segmentation(output_json: PathLike = DEFAULT_EXPORT_FILENAME) -> None

Export video instance segmentation tracks to YouTube-VIS format JSON file.

Parameters:

Name	Type	Description	Default
output_json	PathLike	Optional path to the output JSON file. If not provided, defaults to "youtube_vis_export.json".	DEFAULT_EXPORT_FILENAME