I’m trying to follow [this](  Traffic Light Detection | Pytorch Starter | Kaggle) example to train a FasterRCNN object detection model. A large number of the images in the distribution have no object of interest, and therefore no annotation. However, data augmentation using Albumentations
  File "/usr/local/lib/python3.6/dist-packages/albumentations/augmentations/bbox_utils.py", line 330, in check_bbox
    "to be in the range [0.0, 1.0], got {value}.".format(bbox=bbox, name=name, value=value)
ValueError: Expected x_min for bbox (tensor(nan), tensor(nan), tensor(nan), tensor(nan), tensor(-9223372036854775808)) to be in the range [0.0, 1.0], got nan.

Any suggestions for how best to represent cases where there are no objects of interest?