[maalber]

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.

[dylan604]

> 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.

[nomad86]

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.

[ec109685]

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

[dylan604]

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

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

[arnolmido]

50k manually? How long did it take you?

[nomad86]

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

[maalber]

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

[nomad86]

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.

[BobbyTables2]

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

[karamanolev]

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.

[moralestapia]

RLHF in the wild, nice.