(dataset, predictions, output_folder, **kwargs)
evaluate dataset using different methods based on dataset type.
    Args:
        dataset: Dataset object
        predictions(list[BoxList]): each item in the list represents the
            prediction results for one image.
        output_folder: output folder, to save evaluation files or results.
        **kwargs: other args.
    Returns:
        evaluation result
evaluate dataset using different methods based on dataset type.
    Args:
        dataset: Dataset object
        predictions(list[BoxList]): each item in the list represents the
            prediction results for one image.
        output_folder: output folder, to save evaluation files or results.
        **kwargs: other args.
    Returns:
        evaluation result
evaluate dataset using different methods based on dataset type . Args : dataset : Dataset object predictions ( list [ BoxList ] ) : each item in the list represents the prediction results for one image . output_folder : output folder to save evaluation files or results . ** kwargs : other args . Returns : evaluation result
def evaluate(dataset, predictions, output_folder, **kwargs):
    """evaluate dataset using different methods based on dataset type.
    Args:
        dataset: Dataset object
        predictions(list[BoxList]): each item in the list represents the
            prediction results for one image.
        output_folder: output folder, to save evaluation files or results.
        **kwargs: other args.
    Returns:
        evaluation result
    """
    args = dict(
        dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
    )
    if isinstance(dataset, datasets.COCODataset):
        return coco_evaluation(**args)
    elif isinstance(dataset, datasets.PascalVOCDataset):
        return voc_evaluation(**args)
    elif isinstance(dataset, datasets.WordDataset):
        return word_evaluation(**args)
    else:
        dataset_name = dataset.__class__.__name__
        raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name))
def evaluate ( dataset , predictions , output_folder , * * kwargs ) : args = dict ( dataset = dataset , predictions = predictions , output_folder = output_folder , * * kwargs ) if isinstance ( dataset , datasets . COCODataset ) : return coco_evaluation ( * * args ) elif isinstance ( dataset , datasets . PascalVOCDataset ) : return voc_evaluation ( * * args ) elif isinstance ( dataset , datasets . WordDataset ) : return word_evaluation ( * * args ) else : dataset_name = dataset . __class__ . __name__ raise NotImplementedError ( "Unsupported dataset type {}." . format ( dataset_name ) )