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Comment by maalber | Hacker News Reader

Interesting idea and execution! So basically you wanted to find hotels where the rooms have office chairs and a desk? Or just in any of the images, e.g., a lobby?

Side note; I love how YOLO, a deep learning based model, is now being referred to as traditional object detection. Template matching gang rise up.

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> Or just in any of the images

I just looked at hotels I'm familiar with, and images with highlighted chairs were not limited to the guest rooms. Some are definitely from shared spaces at the hotel.

In our hotel photo database, we have everything: rooms, lobbies, bathrooms, pools, exterior shots of buildings, etc.

I trained an AI to recognize ergonomic chairs, but sometimes there were errors. For example, a chair in a hotel's SPA was always identified as an ergonomic chair. That's why we manually reviewed all 50k photos to verify them.

50k manually? How long did it take you?

I don't know, I assigned this task to trusted specialists from India.

Wow, manually reviewing 50k photos is a lot! Would you be willing to share what the cost of that was?

I created an app similar to Tinder that facilitates manual verification. Around 60 photos can be verified in one minute. The whole process took about a week and didn't cost much.

Sounds like YOLO verified the work of the specialists too! (;->

Doesn't sound that much. When I was playing with datasets, for simple tasks I only took around 3 seconds to classify an image. That's 1200/hour or on the order of 40 hours of work. That can't cost much when outsourced.

RLHF in the wild, nice.

> I trained an AI, but sometimes there were errors.

I edited that down to a proper summary of AI in general

This desk is pretty useless, despite having an office chair: https://www.tripoffice.com/nigeria/lekki-ng/souz-suites-apar...

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