ImageClassificationTask

Task

DashAI.back.tasks.ImageClassificationTask

Task for classifying images into discrete categories.

Image classification predicts categorical labels from image inputs. It accepts image columns as inputs, requires a single categorical output column, and is compatible with image classifier models.

Methods

prepare_for_task(self, dataset: Union[ForwardRef('DatasetDict'), ForwardRef('DashAIDataset')], input_columns: List[str], output_columns: List[str]) -> 'DashAIDataset'

Defined on ImageClassificationTask

Prepare a dataset for an image classification task.

Parameters

dataset : Union[DatasetDict, DashAIDataset]: Dataset to be prepared.
input_columns : List[str]: Names of the image input columns.
output_columns : List[str]: Names of the categorical output columns.

Returns

DashAIDataset: The validated dataset, ready for training or inference.

check_role_names(self, roles: Mapping[str, Any]) -> None

Defined on BaseTask

Refuse columns for a role this task does not declare.

column_roles(cls) -> Tuple[DashAI.back.tasks.column_roles.ColumnRole, ...]

Defined on BaseTask

The roles this task's columns can play, in the order it declares.

Returns

tuple of ColumnRole: The declared roles.

get_metadata(cls) -> Dict[str, Any]

Defined on BaseTask

Return serialisable metadata for the current task.

Parameters

cls : type: The task class (injected automatically by Python for classmethods).

Returns

Dict[str, Any]: Dictionary with keys "inputs_types", "outputs_types", "inputs_cardinality", "outputs_cardinality", "column_roles", "io_roles" and "requires_target".

io_columns(self, roles: Mapping[str, Sequence]) -> DashAI.back.tasks.column_roles.IOColumns

Defined on BaseTask

Translate a session's roles into a model's inputs and outputs.

Parameters

roles : mapping: Role name to the role's columns, as names or as column refs.

Returns

IOColumns: The entries that feed the model as x and as y.

io_roles(cls) -> Dict[str, List[str]]

Defined on BaseTask

Which roles feed a model's inputs and which its outputs.

Returns

dict: {"inputs": [role names], "outputs": [role names]}.

num_labels(self, dataset: 'DashAIDataset', output_column: str) -> int | None

Defined on ClassificationTask

Get the number of unique labels in the output column.

Parameters

dataset : DashAIDataset: Dataset used for training
output_column : str: Output column

Returns

int | None: Number of unique labels or None if not applicable

process_manual_input(self, manual_input: List[dict], dataset_path: str) -> 'DashAIDataset'

Defined on BaseTask

Process manual input data into a DashAIDataset with type validation.

Parameters

manual_input : List[dict]: List of dictionaries representing manual input data.
dataset_path : str: Path to the training dataset (used to get column specs for validation)

Returns

DashAIDataset: Processed DashAIDataset from manual input.

process_predictions(self, dataset: 'DashAIDataset', predictions: 'ndarray', output_column: str) -> 'ndarray'

Defined on ClassificationTask

Process the predictions to return the class labels.

Parameters

dataset : DashAIDataset: Dataset used for training
predictions : np.ndarray: Predictions from the model (probabilities for each class)
output_column : str: Output column

Returns

np.ndarray: Processed predictions with class labels

roles_from_io(self, input_columns: Sequence, output_columns: Sequence) -> Dict[str, list]

Defined on BaseTask

Translate a model's inputs and outputs back into roles.

Parameters

input_columns, output_columns : sequence: The columns fed to the model as x and as y.

Returns

dict: Role name to the role's columns.

validate_dataset_for_task(self, dataset: 'DashAIDataset', dataset_name: str, input_columns: List[str], output_columns: Optional[List[str]]) -> None

Defined on BaseTask

Validate a dataset for the current task.

Parameters

dataset : DashAIDataset: Dataset to be validated
dataset_name : str: Dataset name
input_columns : list of str: Names of the columns selected as inputs.
output_columns : list of str: Names of the columns selected as outputs.

validate_estimated_role(self, role_name: str, columns: Sequence[Tuple[Optional[str], Optional[int]]]) -> None

Defined on BaseTask

Check one role against the columns a preprocessing chain will give it.

Parameters

role_name : str: A declared role.
columns : sequence of (type name, count): One entry per estimated output: the name of its type, or None when unknown, and how many columns it holds, or None when unknown. An unknown count is taken as at least one, and only the role's minimum is checked then.

validate_role(self, role_name: str, columns: Sequence[str], types: Mapping[str, Any]) -> None

Defined on BaseTask

Check one role's columns against its types and cardinality.

Parameters

role_name : str: A declared role.
columns : sequence of str: The names of the role's columns.
types : mapping: The dataset's column types, keyed by column name.

validate_roles(self, roles: Mapping[str, Sequence[str]], types: Mapping[str, Any]) -> None

Defined on BaseTask

Check a session's columns, role by role, against the declaration.

Parameters

roles : mapping: Role name to the names of the role's columns.
types : mapping: The dataset's column types, keyed by column name.

Methods​

Compatible with​

Methods

Compatible with