No, tagging of people is already handled by another model. Gemma just describes what's in the image, and produces a comma separated list of keywords. No additional training is required besides a few tweaks to the prompt so that it outputs just the description, without any "fluff". E.g. it normally prepends such outputs with "Here's a description of the image:" unless you really insist that it should output only the description. I suppose I could use constrained decoding into JSON or something to achieve the same, but I didn't mess with that.

On some images where Gemma3 struggles Mistral Small produces better descriptions, BTW. But it seems harder to make it follow my instructions exactly.

I'm looking forward to the day when I can also do this with videos, a lot of which I also have no interest in uploading to someone else's computer.

Most search rankers do not consider word order, so if you could also append the person's name at the end of text description, it'd probably work well enough for retrieval and ranking at least.

If you want natural language to resolve the names, that'd at a minimum require bounding boxes of the faces and their corresponding names. It'd also require either preprocessing, or specialized training, or both. To my knowledge no locally-hostable model as of today has that. I don't know if any proprietary models can do this either, but it's certainly worth a try - they might just do it. The vast majority of the things they can do is emergent, meaning they were never specifically trained to do them.