[tfsh]

Perhaps you missed the associated documentation? This is a classification tool which requires input labels "uses an EfficientNet architecture and was trained using ImageNet to recognize 1,000 classes, such as trees, animals, food, vehicles".

The full list [1] doesn't seem to include a human. You can tweak the score threshold to reduce false positives.

1: https://storage.googleapis.com/mediapipe-tasks/image_classif...

[rvnx]

You're right about human, that would explain it, but still I find it surprising that such "common item" as a human is not there.

Did you also try on items from the list ?

If there is a match (and this is not frequent), to me it's still very low confidence (like noise or luck).

It seems to be a repacking of https://blog.tensorflow.org/2020/03/higher-accuracy-on-visio...

So an old release from 5 years ago (like very long time in AI-world), and AFAIK it has been superseded by YOLO-NAS and other models. MediaPipe feels really old tool, except for some specific subtasks like face tracking.

And as a side-note, the OKR-system at Google is a very serious thing, there are lot of people internally gaming the system, and that could explain why it is a "new" launch, instead of a rather disappointing rebrand of the 2020-version.

I'd rather recommend building on more modern tools, such as: https://huggingface.co/spaces/HuggingFaceTB/SmolVLM-256M-Ins... (runs on iPhone with < 1GB of memory)

[bigyabai]

> And as a side-note, the OKR-system at Google is a very serious thing, there are lot of people internally gaming the system.

So you came here to offer a knee-jerk assessment of an AI runtime and blamed the failure on OKRs. Then somebody points out that your use-case isn't covered by the model, and you're looping back around to the OKR topic again. To assess an AI inference tool.

Why would you even bother hitting reply on this post if you don't want to talk about the actual topic being discussed? "Agile bad" is not a constructive or novel comment.