Seems reasonable to me.
The workflow for model architectures that TI officially supports and has optimized for the tools is going to be significantly different from custom architectures that don’t have existing export scripts available.
For object detection, I’d recommend YOLOv5, since TI officially supports it. I’ve documented some of these flows (including C++ samples) here: YOLOv5 object detection on BB AI-64: end-to-end walkthrough
For custom architectures/others that TI doesn’t officially support, you’ll need to train your model and then adapt the existing docs and samples for it. You need scripts that export your model, usually as ONNX if you’re using PyTorch, and generates the appropriate metadata for TI’s tools. That’ll be an exercise for you to figure out.