Good post. However, after following your instructions I get the following error in Colab:

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details. 
  warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-5-c60d8a8a6e80> in <module>()
      2 from detecto import core, utils, visualize
      3 image = utils.read_image('images/cat/cat0.jpg')
----> 4 predictions = model.predict(image)
      5 # predictions format: (labels, boxes, scores)
      6 labels, boxes, scores = predictions

7 frames

/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/_utils.py in decode(self, rel_codes, boxes)
    183             box_sum += val
    184         pred_boxes = self.decode_single(
--> 185             rel_codes.reshape(box_sum, -1), concat_boxes
    186         )
    187         return pred_boxes.reshape(box_sum, -1, 4)
RuntimeError: cannot reshape tensor of 0 elements into shape [0, -1] because the unspecified dimension size -1 can be any value and is ambiguous

Any ideas???