natural-language-image-search VS datasets

You have the option of deleting and posting again I think. Anyways good luck.

Wikimedia search seems to work better for most searches I tried, possibly because of the manual tags etc.

https://imagioo.com/?q=astronaut+with+american+flag

https://commons.wikimedia.org/wiki/Special:MediaSearch?type=...

You might want to include examples where your search is better or just a faq on how to use it.

Nice idea though. It does seem to come in handy when you don't have descriptions of images. Eg: https://github.com/haltakov/natural-language-image-search

You can give OpenAI's CLIP a shot: https://github.com/openai/CLIP. It's capable of doing zero-shot classification. Here is a neat example of CLIP usage: https://github.com/haltakov/natural-language-image-search.

Does this use the same codebase as https://github.com/haltakov/natural-language-image-search ? Or do you have a different approach?

CLIP Image Search

Yes, this is definitely possible. You can maybe try computing some kind of image distance between frames or some keyframe extraction.

Once you compute the features, the search is very efficient! I tried it for searching in the 2M photos dataset from Unsplash and it takes like 2-3 seconds: https://github.com/haltakov/natural-language-image-search

I plan to run my personal photos through it :)

Here's a live demo with a simple React frontend. It's searching against an S3 bucket containing Unsplash's open source dataset of 25,000 images, plus a few of my own.

Alright so we are going to use the test dataset that unsplash provides but everything works with the full dataset if you are able to access that one, you can get more information in the github repo.

This project uses GithubActions to assemble a random movie clip every hour or so. Each of the frames is an image taken from the Unsplash Dataset.