Hey, love this write up, thank you.
Id love to read a little more about why onnx was chosen specifically, and what it brings to the game.
There is currently a gap in open and decent models which can be applied to high resolution orthographic imagery, specifically with regards to collection of additional community resilience artefacts such as solar panels and pools. Theres plenty of vendors who want to sell such data, but its so far out of reach of so many worldwide that an open source set of models would go a hell of a long way.
Thanks also for including your PC specs.
On the ONNX choice: it's fairly lightweight to install and runs decently fast on a CPU. Other existing libraries forced me to install torch or tensorflow.
Awesome, thank you.
Does the library inherently handle threading and concurrency to make use of beefier CPUs, or does that need to be coded in to the users Python script? I'm asking as I'll most likely have a go at extending a current project with a custom model or two and am tossing up where to put money in PC hardware. I suspect I'll need to invest both in GPU and CPU, the former for training, and the latter because not everything runs on GPU.
The inference process via ONNX (where most of the CPU time is spent) is multi-threaded. I briefly explored adding multiprocess capabilities for the tiling/downsampling/result merging parts of the codebase, but the improvements were marginal with a trivial implementation, so I didn't explore further (although I'm sure it could be improved).
Great, thank you!