src/transformers/pipelines/object_detection.py/ObjectDetectionPipeline/__call__
class ObjectDetectionPipeline:    def __call__(self, *args, **kwargs) -> Union[Predictions, List[Prediction]]:
        """
        Detect objects (bounding boxes & classes) in the image(s) passed as inputs.

        Args:
            images (:obj:`str`, :obj:`List[str]`, :obj:`PIL.Image` or :obj:`List[PIL.Image]`):
                The pipeline handles three types of images:

                - A string containing an HTTP(S) link pointing to an image
                - A string containing a local path to an image
                - An image loaded in PIL directly

                The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the
                same format: all as HTTP(S) links, all as local paths, or all as PIL images.
            threshold (:obj:`float`, `optional`, defaults to 0.9):
                The probability necessary to make a prediction.

        Return:
            A list of dictionaries or a list of list of dictionaries containing the result. If the input is a single
            image, will return a list of dictionaries, if the input is a list of several images, will return a list of
            list of dictionaries corresponding to each image.

            The dictionaries contain the following keys:

            - **label** (:obj:`str`) -- The class label identified by the model.
            - **score** (:obj:`float`) -- The score attributed by the model for that label.
            - **box** (:obj:`List[Dict[str, int]]`) -- The bounding box of detected object in image's original size.
        """

        return super().__call__(*args, **kwargs)