I’m working on a project for my company in which I need to setup a google cloud VM that is going to be used to train models with Transfer Learning Toolkit and then those models need to run on a Jetson AGX Xavier. For now the first model that needs to be trained with custom data is an instance segmentation model. I’ve been trying to find all the resources necessary to achieve these tasks and so far I have:
- Setup a google cloud VM with nvidia GCP Image
- Train mask r-cnn model following this tutorial until step 4 included (needs to adapt for my VM setup and to load my custom data which is in COCO format).
- Move trained model to Jetson AGX Xavier.
- Convert model using tlt-converter (adapt code from maskrcnn.ipynb in step 2).
- Find python code to make inference with my converted model. maybe from here altought I don’t see examples with instance segmentation.
Am I on the right track here? seems like a lot of work and would really appreciate to not go through this to end up finding out that it wasn’t the right path to follow.