class_docstr: Base class for the task of object detection
  
  Args:
      opts: Command-line arguments
      encoder: Image-encoder model (e.g., MobileNet or ResNet)
docstr: Add model specific arguments
docstr_tok: ["Add","model","specific","arguments"]
code: def add_arguments(
      cls, parser: argparse.ArgumentParser
  ) -> argparse.ArgumentParser:
      """Add model specific arguments"""
      if cls != BaseDetection:
          # 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(
          "--model.detection.name",
          type=str,
          default=None,
          help="Detection model name",
      )
      group.add_argument(
          "--model.detection.n-classes",
          type=int,
          default=80,
          help="Number of classes in the dataset. Defaults to 80.",
      )
      group.add_argument(
          "--model.detection.pretrained",
          type=str,
          default=None,
          help="Path of the pretrained detection model. Defaults to None.",
      )
      group.add_argument(
          "--model.detection.output-stride",
          type=int,
          default=None,
          help="Output stride of the classification network. Defaults to None.",
      )
      group.add_argument(
          "--model.detection.replace-stride-with-dilation",
          action="store_true",
          default=False,
          help="Replace stride with dilation",
      )
      group.add_argument(
          "--model.detection.freeze-batch-norm",
          action="store_true",
          default=False,
          help="Freeze batch norm layers in detection model. Defaults to False.",
      )
  
      return parser
  
file_path: ml-cvnets/cvnets/models/detection/base_detection.py
imports: import argparse
  from typing import Dict
  from torch import nn
  from cvnets.misc.init_utils import initialize_weights
  from cvnets.models import MODEL_REGISTRY, BaseAnyNNModel, get_model
  from cvnets.models.classification.base_image_encoder import BaseImageEncoder
  from utils import logger
label_desc: Use image node_id 2 for calling the BaseDetection obj's underlying member method code with example usage: obj.add_arguments(cls, parser) and returns: parser, parser