I am trying to train a detectnet model on the COCO dataset. I have downloaded the entire dataset, and I have run the coco2kitti.py 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: https://github.com/dusty-nv/jetson-inference/blob/master/data/networks/detectnet.prototxt[/]
- pretrained: bvlc_googlenet.caffemodel