I am running Google's tensorflow object-detection API's jupyter notebook on an Ubuntu 16.04 Parallels desktop on my Mac. I wanted to test out one of the non-default models (i.e. not SSD with Mobilenet) to see how the accuracy of the bounding boxes may change on an object-detection task.
I changed the section on Model Preparation in the notebook as follows:
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_NAME = 'ssd_inception_v2_coco_11_06_2017'
MODEL_NAME = 'rfcn_resnet101_coco_11_06_2017'
#MODEL_NAME = 'faster_rcnn_resnet101_coco_11_06_2017'
#MODEL_NAME = 'faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
I then jump to executing the cell that loads the frozen Tensorflow model into memory. Unfortunately, if I try any of the last 3 models (rfcn_resnet101_coco_11_06_2017, faster_rcnn_resnet101_coco_11_06_2017, faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017), the notebook crashes in Firefox and I get the following error message:
The kernel appears to have died. It will restart automatically.
So I am unable to test out the last 3 models, even though I have downloaded the tar.gz files and extracted them in the object_detection folder. Could somebody please explain what I may be doing wrong?
Thank you for your time!
