to_fiftyone#

DetectionEvaluator.to_fiftyone(name: str | None = None, record_fo_ids: bool = False, existing: Literal['error', 'update', 'erase'] = 'error') → fo.Dataset#

Convert evaluator to fiftyone.

Convert the detection evaluator into a fiftyone dataset, that can then be inspected with Fiftyone’s webapp. The resulting dataset will have the groundtruth sample field, along with all the prediction set’s name and value in the self.predictions_dictionary attribute

Parameters:

  • name – Name of the fiftyone dataset to add the samples to. If the dataset does not exist, it will be created. If set to None, will use self.image_root folder name

  • record_fo_ids – whether to record the fiftyone ids of samples and annotations. If set to True, will create fo_id column in self.images and fo_id and is_keypoint column in dataframes contained in self.predictions_dictionary and self.groundtruth to be able to reindex them in the created fiftyone dataset.

  • existing –

    What to do in case there is already a fiftyone dataset with the same name.

    • ”error”: will raise an error.

    • ”erase”: will erase the existing dataset before uploading

      this one

    • ”update”: will try to update the dataset by fusing together samples

      with the same “relative_path”

    Defaults to “error”.

Returns:

Fiftyone one dataset that can then be used to launch the webapp with fiftyone.launch_app(evaluator.to_fiftyone("dataset"))

Return type:

fiftyone.core.dataset.Dataset

See also