Best software for semantic search across photos and videos on Windows + Synology NAS?
Hi guys, I'm looking for a way to index my entire media archive across both a Windows 11 PC/laptop and a Synology NAS, then search it semantically (probably with AI) based on what's actually visible in the photos and videos. For example: If I have a photo…
Hi guys, I'm looking for a way to index my entire media archive across both a Windows 11 PC/laptop and a Synology NAS, then search it semantically (probably with AI) based on what's actually visible in the photos and videos. For example: If I have a photo buried somewhere in a random folder that contains a pizza, searching for "pizza" would return that image. If I have a video showing a car driving by, searching for "car", "car driving", or something similar would return that video. Ideally, the software would: Run locally on a Windows laptop or desktop. Let me specify which drives, folders, or network locations to index (similar to how Everything indexes selected directories). Index existing folders without moving or renaming my files. Work with both photos and videos. Automatically recognize objects, scenes, and people without requiring manual tagging. Support libraries stored on a Synology NAS as well as local Windows drives. Scale well to a very large archive (10s of thousands of photos and videos). Is AI-powered semantic search at the point where this is practical? I was considering manually tagging my entire archive, but that would take a lot of time, so I'm wondering if there are other solutions that that can handle this automatically. I'd be interested to hear what people are using and how well it works in real-world use. Thanks.
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Best guess is to find an available locally hosted system trained with an object image database (hopefully there an open one exists) that your current hardware can run. Find out how long it takes to process a few images, then figure what type of hardware is needed to process all of them. There are a bunch of GPUs available for local hosting that aren't the current "$10k or more each" (this means they are used and nearly 10 years old and nearing end of life. Not a good market right now). Run the numbers before buying to see if they should be able to process the pictures in a reasonable length of time. And remember, it will take just as long to add any other tagging, unless you can somehow acquire a large stash of more recent GPUs. Don't forget this also puts a hard limit on how quickly your picture horde can grow and remain indexed. I'm guessing the tricky part will be finding the pre-trained image that you can currently run. If you can find that, proceeding to stage 2 and finding if this is remotely possible shouldn't be that much of an issue (of course it will be an issue, expect even a docker image to complain about something. But it will also give you a definitive yes/no on if this project is viable).
immich It's a docker container, but runs on linux/windows, and allows semantic search on media that it ingests. Tag a face with a name and now that person is relatively name searchable in your private photo store. It's developed by FUTO, so privacy focused/self-hosted without cloud strings. Freely available, pay for a license if you feel like it but not required.
immich runs locally and does object scene recognition reasonably well at this scale,free and self hostable on synology as for attaching sematic entity relationships across your achive rather than flat embeddings graph database tools exist though that's developer territory.digikam handles tagging but the ai side is weaker
Automatically recognize objects, scenes, and people without requiring manual tagging. and Run locally Nope. Unless you happen to have a local server already trained with an object image database of at least a few hundred million images. So unless you've got several full size 19-inch racks loaded up with AI hardware (like tens of those Nvidia cards that run $10k each), it ain't gonna happen.