datamint.api.dto

class datamint.api.dto.BoxPrompt(*, x_min, y_min, x_max, y_max)

Bases: BaseModel

A bounding box prompt, in original image pixel space.

Parameters:

  • x_min (float)

  • y_min (float)

  • x_max (float)

  • y_max (float)

model_config: ClassVar[ConfigDict] = {'extra': 'ignore'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

x_max: float
x_min: float
y_max: float
y_min: float
class datamint.api.dto.CreateAnnotationDto(type, identifier, scope, annotation_worklist_id=None, value=None, imported_from=None, import_author=None, frame_index=None, is_model=None, model_id=None, geometry=None, units=None, source=None, frame_range=None)

Bases: object

Parameters:

  • type (AnnotationType | str)

  • identifier (str)

  • scope (str)

  • annotation_worklist_id (str | None)

  • imported_from (str | None)

  • import_author (str | None)

  • frame_index (int | None)

  • is_model (bool | None)

  • model_id (str | None)

  • geometry (Geometry | None)

  • units (str | None)

  • source (str | None)

  • frame_range (tuple[int, int] | None)

to_dict()
Return type:

dict[str, Any]

class datamint.api.dto.InferencePrompts(*, text=None, points=None, boxes=None)

Bases: BaseModel

Prompts for promptable segmentation models (e.g. SAM3-based).

At least one of text, points, or boxes must be provided. Whether a given deployed model actually uses the prompts is up to that model; a model that doesn’t support prompts simply ignores them.

Parameters:
boxes: list[BoxPrompt] | None
model_config: ClassVar[ConfigDict] = {'extra': 'ignore'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

points: list[PointPrompt] | None
text: str | None
to_dict()

Return a JSON-compatible dict with only the set fields.

Return type:

dict

class datamint.api.dto.PointPrompt(*, label, x, y)

Bases: BaseModel

A single point prompt, in original image pixel space.

label

1 for foreground, 0 for background.

Type:

int

x

X coordinate.

Type:

float

y

Y coordinate.

Type:

float

Parameters:

  • label (int)

  • x (float)

  • y (float)

label: int
model_config: ClassVar[ConfigDict] = {'extra': 'ignore'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

x: float
y: float
class datamint.api.dto.SaveResultsOptions(*, worklist_id=None, annotation_source='model_deploy', imported_from=None, author_email=None)

Bases: BaseModel

Configuration for saving inference results as annotations on Datamint.

These options are only applied when save_results=True on the prediction request.

worklist_id

Annotation worklist ID to associate saved annotations with.

Type:

str | None

annotation_source

Source tag for saved annotations. Use 'model_deploy' for deployed model inference or 'model_pipeline' for pipeline-based runs.

Type:

Literal['model_pipeline', 'model_deploy'] | None

imported_from

Free-text describing the origin or context of this inference run.

Type:

str | None

author_email

Email to attribute as the author of saved annotations. Defaults to the API key owner if not provided.

Type:

str | None

Parameters:

  • worklist_id (str | None)

  • annotation_source (Literal['model_pipeline', 'model_deploy'] | None)

  • imported_from (str | None)

  • author_email (str | None)

annotation_source: Literal['model_pipeline', 'model_deploy'] | None
author_email: str | None
imported_from: str | None
model_config: ClassVar[ConfigDict] = {'extra': 'ignore'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

to_dict()

Return a dict with only the set (non-None) fields.

Return type:

dict[str, str]

worklist_id: str | None