class_docstr: Base Dataset class for Object Dection datasets.
  
  Args:
      opts: Command-line arguments
class_docstr_tok: ["Base","Dataset","class","for","Object","Dection","datasets",".","Args",":","opts",":","Command-line","arguments"]
code: def add_arguments(
      cls, parser: argparse.ArgumentParser
  ) -> argparse.ArgumentParser:
      if cls != BaseDetectionDataset:
          # Don't re-register arguments in subclasses that don't override `add_arguments()`.
          return parser
  
      group = parser.add_argument_group(cls.__name__)
  
      group.add_argument(
          "--evaluation.detection.save-overlay-boxes",
          action="store_true",
          help="enable this flag to visualize predicted masks on top of input image",
      )
      group.add_argument(
          "--evaluation.detection.mode",
          type=str,
          default="validation_set",
          required=False,
          choices=["single_image", "image_folder", "validation_set"],
          help="Contribution of mask when overlaying on top of RGB image.",
      )
      group.add_argument(
          "--evaluation.detection.path",
          type=str,
          default=None,
          help="Path of the image or image folder (only required for single_image and image_folder modes).",
      )
      group.add_argument(
          "--evaluation.detection.num-classes",
          type=int,
          default=None,
          help="Number of segmentation classes used during training.",
      )
      group.add_argument(
          "--evaluation.detection.resize-input-images",
          action="store_true",
          default=False,
          help="Resize input images to fixed size during detection evaluation.",
      )
  
      return parser
  
imports: import argparse
  from data.datasets import BaseImageDataset
label_desc: Use image node_id 2 for calling the BaseDetectionDataset obj's underlying member method code with example usage: obj.add_arguments(cls, parser) and returns: parser, parser