Today I'd like to share my open source project, Moonshine!

Pretrained vision models are a popular way to reduce how much data you need and to speed up training, but for remote sensing (i.e. aerial, satellite) it can be a challenge to find good weights. Why use Moonshine?

1. Pretrained on multispectral data: Existing popular CV packages are nice to use but are trained on ImageNet or similar, meaning that not only is the domain of the pretraining different, but you may be restricted to only 3 channels. For remote sensing platforms with multispectral data, this might be a non-starter. Moonshine includes models specifically trained for your remote sensing problems.

2. Focus on usability: There are academic releases of specialized models that do support remote sensing data, but often they are difficult to use. They might be hard to install, and often are supported by a grad student who is more focused on publication than software. A core tenant of Moonshine is that it should be easy to use.

I've been working on this project for nearly a year now and I'm really excited to show it off and most importantly get feedback! I have big plans for what to build in the future, but this set of features was the smallest one I could think of that would provide some value.

Docs: https://moonshineai.readthedocs.io/en/latest/index.html Github: https://github.com/moonshinelabs-ai/moonshine