Dataset

Bases: BaseModel

A dataset class representing a dataset in the Tenyks platform

Attributes:

Name	Type	Description
`client`	`Client`	The client to interact with the Tenyks API.
`workspace_name`	`str`	Name of the workspace the dataset belongs to.
`key`	`str`	Key of the dataset.
`name`	`str`	Name of the dataset.
`owner`	`str`	Owner of the dataset.
`owner_email`	`EmailStr`	Owner email of the dataset.
`created_at`	`datetime`	Creation timestamp of the dataset.
`images_location`	`Optional [ Union [ AWSLocation , AzureLocation , GCSLocation ]]`	Directory location of the images of the dataset.
`metadata_location`	`Optional [ Union [ AWSLocation , AzureLocation , GCSLocation ]]`	Directory location of the metadata of the dataset.
`categories`	`List [ Category ]`	Categories/classes of the dataset.
`models`	`List`	Names of the models of the dataset.
`status`	`str`	Status of the dataset.
`n_images`	`int`	Number of images in the dataset.
`iou_threshold`	`float`	IOU threshold set for the dataset.

add_image

add_image ( image_path , annotations = None , tags = None , verbose = False )

Add an image to the dataset along with its annotations and tags.

Parameters:

Name	Type	Description	Default
`image_path`	`str`	The path of the image to add.	required
`annotations`	`Optional [ List [ Annotation ]]`	The annotations to add to the image. Defaults to None.	`None`
`tags`	`Optional [ List [ Tag ]]`	The tags to add to the image. Defaults to None.	`None`
`verbose`	`Optional [bool]`	If True, provides progress updates. Defaults to False.	`False`

count_images

count_images ( filter = None , model_key = None )

Return image count that match the filter criteria.

Parameters:

Name	Type	Description	Default
`filter`	`Optional [str]`	Filter conditions for counting. Defaults to None.	`None`
`model_key`	`Optional [str]`	Model key to filter images. Defaults to None.	`None`

Returns:

Name	Type	Description
`int`	`int`	Number of images that match the filter criteria.

create_model

create_model ( name , confidence_threshold = None , iou_threshold = None )

Create a new model for the dataset.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the new model.	required
`confidence_threshold`	`Optional [float]`	The confidence threshold for the model. Defaults to None.	`None`
`iou_threshold`	`Optional [float]`	The IOU threshold for the model. Defaults to None.	`None`

Returns:

Name	Type	Description
`Model`	`Model`	The newly created model.

delete_model

delete_model ( key )

Delete a model from the dataset.

Parameters:

Name	Type	Description	Default
`key`	`str`	The key of the model to delete.	required

get_category_by_id

get_category_by_id ( category_id )

Retrieve a category by its ID.

Parameters:

Name	Type	Description	Default
`category_id`	`int`	The ID of the category to retrieve.	required

Returns:

Name	Type	Description
`Category`	`Category`	The category corresponding to the given ID.

get_category_by_name

get_category_by_name ( category_name )

Retrieve a category by its name.

Parameters:

Name	Type	Description	Default
`category_name`	`str`	The name of the category to retrieve.	required

Returns:

Name	Type	Description
`Category`	`Category`	The category corresponding to the given name.

get_image_by_key

get_image_by_key ( image_key )

Retrieve an image by its key.

Parameters:

Name	Type	Description	Default
`image_key`	`str`	The key of the image to retrieve.	required

Returns:

Name	Type	Description
`Image`	`Image`	The image corresponding to the given key.

get_model

get_model ( key )

Retrieve a model by its key.

Parameters:

Name	Type	Description	Default
`key`	`str`	The key of the model to retrieve.	required

Returns:

Name	Type	Description
`Model`	`Model`	The model corresponding to the given key.

get_model_names

get_model_names ()

Retrieve the names of the models associated with the dataset.

Returns:

Type	Description
`List [str]`	List[str]: A list of model display names.

get_models

get_models ()

Retrieve the models associated with the dataset.

Returns:

Type	Description
`List [ Model ]`	List[Model]: A list of models associated with the dataset.

get_tag_by_key

get_tag_by_key ( tag_key )

Retrieve a tag by its key.

Parameters:

Name	Type	Description	Default
`tag_key`	`str`	The key of the tag to retrieve.	required

Returns:

Name	Type	Description
`Tag`	`Tag`	The tag corresponding to the given key.

get_tag_by_name

get_tag_by_name ( tag_name )

Retrieve a tag by its display name.

Parameters:

Name	Type	Description	Default
`tag_name`	`str`	The name of the tag to retrieve.	required

Returns:

Name	Type	Description
`Tag`	`Tag`	The tag corresponding to the given display name.

get_tags

get_tags ()

Retrieve the tags associated with the dataset.

Returns:

Type	Description
`List [ Tag ]`	List[Tag]: A list of tags created for the dataset.

head

head ( n = 5 )

Retrieve the first few images from the dataset.

Parameters:

Name	Type	Description	Default
`n`	`int`	The number of images to retrieve. Defaults to 5.	`5`

Returns:

Type	Description
`List [ Image ]`	List[Image]: A list of the first `n` images in the dataset.

images_generator

images_generator ( filter = None , sort_by = None , model_key = None , page_size = 250 )

Generator to retrieve images from the dataset in a paginated manner.

Parameters:

Name	Type	Description	Default
`filter`	`Optional [str]`	Filter conditions for the search. Defaults to None.	`None`
`sort_by`	`Optional [str]`	Sort criteria for the search. Defaults to None.	`None`
`model_key`	`Optional [str]`	Model key to filter images. Defaults to None.	`None`
`page_size`	`Optional [int]`	Number of images per page. Defaults to 250.	`250`

Yields:

Name	Type	Description
`Generator`	`Generator`	A generator yielding images.

ingest

ingest ( import_operation = None , verbose = True )

Trigger the ingestion process for the dataset.

Parameters:

Name	Type	Description	Default
`import_operation`	`Optional [str]`	The import operation type. Defaults to None.	`None`
`verbose`	`Optional [bool]`	If True, provides progress updates. Defaults to True.	`True`

search_images

search_images ( n_images = 250 , filter = None , sort_by = None , model_key = None )

Perform image search in the dataset based on filters.

Parameters:

Name	Type	Description	Default
`n_images`	`Optional [int]`	The number of images to retrieve. Defaults to 250.	`250`
`filter`	`Optional [str]`	Filter conditions for the search. Defaults to None.	`None`
`sort_by`	`Optional [str]`	Sort criteria for the search. Defaults to None.	`None`
`model_key`	`Optional [str]`	Model key to filter images. Defaults to None.	`None`

Returns:

Type	Description
`List [ Image ]`	List[Image]: A list of images that match the search criteria.

update_image

update_image ( image_key , annotations , tags = None , verbose = False )

Update an existing image's annotations and tags.

Parameters:

Name	Type	Description	Default
`image_key`	`str`	The key of the image to update.	required
`annotations`	`List [ Annotation ]`	The new annotations for the image.	required
`tags`	`Optional [ List [ Tag ]]`	The new tags for the image. Defaults to None.	`None`
`verbose`	`Optional [bool]`	If True, provides progress updates. Defaults to False.	`False`

upload_annotations

upload_annotations ( coco_path_or_dict , verbose = True )

Upload annotations to the dataset.

Parameters:

Name	Type	Description	Default
`coco_path_or_dict`	`Union [str, dict]`	The file path or dictionary of COCO annotations to upload.	required
`verbose`	`Optional [bool]`	If True, provides progress updates. Defaults to True.	`True`

upload_annotations_from_cloud

upload_annotations_from_cloud ( coco_file_location )

Upload annotations to the dataset from a cloud location.

Parameters:

Name	Type	Description	Default
`coco_file_location`	`Union [ AWSLocation , AzureLocation , GCSLocation ]`	The cloud location of the COCO annotations to upload.	required

upload_custom_embeddings

upload_custom_embeddings ( embedding_type , embedding_name , embedding_location , embedding_filename = None , file_extension = 'arrow' )

Upload custom embeddings to the dataset for use in Embedding viewer.

Parameters:

Name	Type	Description	Default
`embedding_type`	`str`	The type of embeddings. At present only 'images' is supported. 'annotations'/'predictions' coming soon!	required
`embedding_name`	`str`	The display name of the embeddings.	required
`embedding_location`	`dict`	The location of the embeddings in cloud storage.	required
`embedding_filename`	`str`	The filename for JSON conversion. Defaults to None.	`None`
`file_extension`	`str`	The file extension (either 'json' or 'arrow'). Defaults to "arrow".	`'arrow'`

upload_custom_embeddings_from_local

upload_custom_embeddings_from_local ( embedding_type , embedding_name , embedding_filepath , embedding_filename = None , file_extension = 'json' )

Upload custom embeddings from a local file to the dataset.

Parameters:

Name	Type	Description	Default
`embedding_type`	`str`	The type of embeddings. At present only 'images' is supported. 'annotations'/'predictions' coming soon!	required
`embedding_name`	`str`	The display name of the embeddings.	required
`embedding_filepath`	`str`	The path to the local file containing the embeddings.	required
`embedding_filename`	`str`	The filename for JSON conversion. Defaults to None.	`None`
`file_extension`	`str`	The file extension (only 'json' is supported for local uploads). Defaults to "json".	`'json'`

upload_images

upload_images ( image_directory_or_paths , verbose = True )

Upload images to the dataset.

Parameters:

Name	Type	Description	Default
`image_directory_or_paths`	`Union [str, Path , List [str]]`	The directory or paths of the images to upload.	required
`verbose`	`Optional [bool]`	If True, provides progress updates. Defaults to True.	`True`