class_docstr: Describes backbone information and provides utility functions.
      
class_docstr_tok: ["Describes","backbone","information","and","provides","utility","functions","."]
docstr: Downloads ImageNet weights and returns path to weights file.
          
docstr_tok: ["Downloads","ImageNet","weights","and","returns","path","to","weights","file","."]
code: def download_imagenet(self):
      """Downloads ImageNet weights and returns path to weights file."""
      resnet_filename = "ResNet-{}-model.keras.h5"
      resnet_resource = "https://github.com/fizyr/keras-models/releases/download/v0.0.1/{}".format(
          resnet_filename
      )
      depth = int(self.backbone.replace("resnet", ""))
  
      filename = resnet_filename.format(depth)
      resource = resnet_resource.format(depth)
      if depth == 50:
          checksum = "3e9f4e4f77bbe2c9bec13b53ee1c2319"
      elif depth == 101:
          checksum = "05dc86924389e5b401a9ea0348a3213c"
      elif depth == 152:
          checksum = "6ee11ef2b135592f8031058820bb9e71"
  
      return keras.utils.get_file(
          filename, resource, cache_subdir="models", md5_hash=checksum
      )
  
file_path: ImageAI/imageai_tf_deprecated/Detection/keras_retinanet/models/resnet.py
imports: from tensorflow import keras
  import keras_resnet
  import keras_resnet.models
  from .None import retinanet
  from .None import Backbone
  from ..utils.image import preprocess_image
label_desc: Use image node_id 3 for calling the ResNetBackbone obj's underlying member method code with example usage: obj.download_imagenet() and returns: keras