tensorflow/models · error · FileNotFoundError

Dataset {dataset_name!r} is missing required input folder: {

Error message

Dataset {dataset_name!r} is missing required input folder: {input_dir}

What it means

Error "Dataset {dataset_name!r} is missing required input folder: {input_dir}" thrown in tensorflow/models.

Source

Thrown at official/projects/waste_identification_ml/data_generation/auto_labeler_pipeline/segmentation.py:192

) -> list[tuple[str, str]]:
  """Validates that each dataset has the expected input layout.

  Args:
      dataset_directories: List of ``(dataset_name, dataset_path)`` tuples.
      train_val_folder_name: Name of the train/val input subfolder.

  Returns:
      A list of ``(dataset_name, input_dir)`` tuples ready for processing.

  Raises:
      FileNotFoundError: If a dataset is missing its input folder.
  """
  validated = []
  for dataset_name, dataset_path in dataset_directories:
    input_dir = os.path.join(dataset_path, train_val_folder_name)

    if not os.path.isdir(input_dir):
      raise FileNotFoundError(
          f"Dataset {dataset_name!r} is missing required input folder: "
          f"{input_dir}"
      )

    validated.append((dataset_name, input_dir))

  return validated


def validate_classifier_output_dir(classifier_output_dir: str) -> None:
  """Ensures the classifier output directory does not already exist.

  Args:
      classifier_output_dir: Path to the classifier dataset directory.

  Raises:
      FileExistsError: If the classifier output directory already exists.
  """

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/projects/waste_identification_ml/data_generation/auto_labeler_pipeline/segmentation.py:192 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/01342250ee2e0057. Report an issue: GitHub.