How to train detectnet on COCO dataset

I am trying to train a detectnet model on the COCO dataset. I have downloaded the entire dataset, and I have run the script to generate the label files from the COCO annotations. I have made a subset of the data about ~6,000 images for training and ~1000 images for validation.

I performed the rest of the steps per a couple of tutorials, yet my training does not seem to be yielding any results at all. In fact, the charts in Digits only show “loss_bbox(train)” and “loss_coverage(train)”. There is no mAP line, precision line, or any other line on my charts. Has anyone had these issues or successfully trained detectnet on the COCO dataset? Is there something I am missing in data preparation?

I loaded the dataset up to Digits

  • Padding: 640x640
  • Minimum box size: 25
  • custom classes: dontcare,person
  • rest = defaults

I created an object detection model

  • epochs: 100
  • subtract mean: none
  • Solver: adam
  • learning rate: 2.5e-05
  • Policy: exponential decay
  • Custom Network:[/]
  • pretrained: bvlc_googlenet.caffemodel