For info, the same tool works well with 2 million images found in the Unsplash dataset [1]. Features only have to be computed once for the dataset, and only the feature vector for the user query has to be computed on the fly. Then matching features can be done in a manner that scales well.

So, the present tool does not scale because the videos are part of the user query, but a company with an easy access to the videos and the computational power to pre-encode the frames as features could create a search engine based on CLIP.

[1] https://github.com/haltakov/natural-language-image-search

Thanks for sharing! :)

Yes, the feature computation on the images has to be sone only once and the representation is very efficient - 512 float16 values per image.

Yes, I know. :D Your previous project with Unsplash made me try a similar approach [1] for banners of video games on Steam.

[1] https://github.com/woctezuma/steam-image-search

Cool application! I wasn't aware that there are so many images available on Steam...

Yes, Steam has grown a lot. Last I checked, there were ~50k apps.

Edit: 50,630 apps according to https://www.gamedatacrunch.com/

As I focused on vertical banners, the list was smaller (~30k apps). This is equivalent to the *lite* version of Unsplash's dataset.