PhotoStructure is entirely self-hosted, including model training and application: the public domain base models (trained on huge datasets) are fetched and cached locally.
By design, none of your data (or even metadata) leaves your server.
(I expect to ship this in an upcoming beta next month.)
I want to label all the faces in the photos I've taken since 1997, and save them in the metadata. I'll be glad to run it against my photos. Windows 10, WSL, and/or Virtual Machine with Linux of your choice.
I've got desktop builds for macOS, Windows, and Linux, as well as "headless" builds for Docker and even "directly" via Node.js. Instructions here: https://photostructure.com/install
Nice! Will try this out. Are you planning on taking advantage of in-built neural engines like that in Apple M1 for speeding up object/facial recognition?
I'd like to, but practically speaking, I'm at the mercy of native support in the libraries I'm using. If support is added, though, it's trivial for me to add the switch as a user-definable setting.
What I'm building into PhotoStructure is typically called "transfer learning."
https://en.wikipedia.org/wiki/Transfer_learning
PhotoStructure is entirely self-hosted, including model training and application: the public domain base models (trained on huge datasets) are fetched and cached locally.
By design, none of your data (or even metadata) leaves your server.
(I expect to ship this in an upcoming beta next month.)