Is there anything like this also supporting other GPUs? Thinking of Apple Silicon or embedded ones in phones etc.

You're right, UncleEntity, thanks for highlighting that. My phrasing could have been clearer. AGPL does allow various uses, including commercial, provided its terms are met.

Our intention with LightlyTrain (AGPL/Commercial license option) is to offer a streamlined, production-ready pretraining engine. This contrasts with our other library, LightlySSL (github.com/lightly-ai/lightly), which is MIT-licensed and geared towards researchers needing flexible building blocks.

We found many companies wanted a simpler "it just works" solution for pretraining, which is why LightlyTrain exists with its specific licensing options tailored for commercial teams alongside the AGPL.

Thanks again for the clarification!

Hi Sonnigeszeug, great that you're looking into LightlyTrain!

We designed LightlyTrain specifically for production teams who need a robust, easy-to-use pretraining solution without getting lost in research papers. It builds on learnings from our MIT-licensed research framework, LightlySSL (github.com/lightly-ai/lightly), but is tailored for scalability and ease of integration.

For commercial use where the AGPL terms might not fit your needs, we offer straightforward commercial licenses for LightlyTrain. Happy to chat more if that's relevant for you!

Thanks for the kind words, joelio182! Glad you see the value in making SSL more practical for real-world domain shift issues.

As liopeer mentioned, we have results for medical (DeepLesion) and agriculture (DeepWeeds) in the blog post. We haven't published specific benchmarks on satellite or industrial inspection data yet, but those are definitely the kinds of niche domains where pretraining on specific unlabeled data should yield significant benefits. We're keen to explore more areas like these.

Our goal is exactly what you pointed out - bridging the gap between SSL research and practical application where labels are scarce. Appreciate the encouragement!