class_docstr: Detection dataset loaded from LST file and raw images.
  LST file is a pure text file but with special label format.
  
  Checkout :ref:`lst_record_dataset` for tutorial of how to prepare this file.
  
  Parameters
  ----------
  filename : type
      Description of parameter `filename`.
  root : str
      Relative image root folder for filenames in LST file.
  flag : int, default is 1
      Use 1 for color images, and 0 for gray images.
  coord_normalized : boolean
      Indicate whether bounding box coordinates haved been normalized to (0, 1) in labels.
      If so, we will rescale back to absolute coordinates by multiplying width or height.
class_docstr_tok: ["Detection","dataset","loaded","from","LST","file","and","raw","images",".","LST","file","is","a","pure","text","file","but","with","special","label","format",".","Checkout",":","ref",":","`","lst_record_dataset","`","for","tutorial","of","how","to","prepare","this","file",".","Parameters","--","--","--","--","--","filename",":","type","Description","of","parameter","`","filename","`",".","root",":","str","Relative","image","root","folder","for","filenames","in","LST","file",".","flag",":","int",",","default","is","1","Use","1","for","color","images",",","and","0","for","gray","images",".","coord_normalized",":","boolean","Indicate","whether","bounding","box","coordinates","haved","been","normalized","to","(","0",",","1",")","in","labels",".","If","so",",","we","will","rescale","back","to","absolute","coordinates","by","multiplying","width","or","height","."]
code: def __init__(self, filename, root="", flag=1, coord_normalized=True):
      self._flag = flag
      self._coord_normalized = coord_normalized
      self._items = []
      self._labels = []
      full_path = os.path.expanduser(filename)
      with open(full_path) as fin:
          for line in iter(fin.readline, ""):
              line = line.strip().split("\t")
              label = np.array(line[1:-1]).astype("float")
              im_path = os.path.join(root, line[-1])
              self._items.append(im_path)
              self._labels.append(label)
  
file_path: gluon-cv/gluoncv/data/lst/detection.py
imports: from __future__ import absolute_import
  import os
  import numpy
  import mxnet
  from mxnet.gluon.data import Dataset
  from ..recordio.detection import _transform_label
label_desc: Use image node_id 1 to create a new LstDetection object from inherited base classes: Dataset with example: obj = LstDetection(filename, root, flag, coord_normalized)