class_docstr: XML dataset for detection.
  
  Args:
      min_size (int | float, optional): The minimum size of bounding
          boxes in the images. If the size of a bounding box is less than
          ``min_size``, it would be add to ignored field.
docstr: Filter images too small or without annotation.
docstr_tok: ["Filter","images","too","small","or","without","annotation","."]
code: def _filter_imgs(self, min_size=32):
      """Filter images too small or without annotation."""
      valid_inds = []
      for i, img_info in enumerate(self.data_infos):
          if min(img_info["width"], img_info["height"]) < min_size:
              continue
          if self.filter_empty_gt:
              img_id = img_info["id"]
              xml_path = osp.join(
                  self.img_prefix, "Annotations", f"{img_id}.xml"
              )
              tree = ET.parse(xml_path)
              root = tree.getroot()
              for obj in root.findall("object"):
                  name = obj.find("name").text
                  if name in self.CLASSES:
                      valid_inds.append(i)
                      break
          else:
              valid_inds.append(i)
      return valid_inds
  
file_path: insightface/detection/scrfd/mmdet/datasets/xml_style.py
imports: import os.path
  import xml.etree.ElementTree
  import mmcv
  import numpy
  from PIL import Image
  from .builder import DATASETS
  from .custom import CustomDataset
label_desc: Use image node_id 3 for calling the XMLDataset obj's underlying member method code with example usage: obj._filter_imgs(min_size) and returns: valid_inds