ubicloud VS clip-retrieval

The docs still say the Elastic license is used but looking at https://github.com/ubicloud/ubicloud/blob/main/LICENSE it looks like the project might have switched to GNU Affero General Public License v3.0 in the last day.

At Ubicloud, we have 100% line and branch coverage that is mandated on every PR (https://github.com/ubicloud/ubicloud). We also have an E2E test suite that we run periodically and with every commit. We did not really feel like our tests are slowing us down, it actually makes us faster since we have a higher trust to the payload and many manual checks that would need to be done is safely skipped.

> Taken from here: https://ubicloud.com/

Am I the only one getting a certificate error browsing there?

Link to our project: https://github.com/ubicloud/ubicloud

We’re choosing Elastic V2 for three reasons: (1) We’re planning to monetize through a managed service and we’d like the license to support that, (2) Later if we change our mind, we think it’s easier on our users if we go from a restrictive license to a more permissive one, and (3) The Elastic V2 license is much simpler than its cousin, Server Side Public License (SSPL).

That said, Elastic V2 is a new license and doesn’t seem to as popular as SSPL. Also, some projects out there mix and match multiple licenses in their repo to be able to call themselves open source.

Any insights / feedback on Elastic V2 or software licenses in general?

The tool clip-retreival lets you filter those 400 million images to whatever subsets you're interested in --- for example, 10,000 images of (mostly) handwriting.

Hehe, well you know, PR welcome, the front end is 500 lines https://github.com/rom1504/clip-retrieval/blob/main/front/sr...

Other people have done a few alternate front ends already

This one is meant to be functional, but could sure be made prettier

This might work: https://github.com/rom1504/clip-retrieval .

HaveIBeenTrained uses clip retrieval to search the Laion-5B and Laion-400M image datasets. These are currently the largest public text-to-image datsets, and they are used to train models like Stable Diffusion, Imagen, among many others.

Done https://github.com/rom1504/clip-retrieval/commit/53e3383f58b...

Using clip for searching is better than direct text indexing for a variety of reasons but here for example because it matches better what stable diffusion sees

Based on OpenAI's CLIP and the clip-retrieval library (https://github.com/rom1504/clip-retrieval), I've built an end-to-end demo for a semantic and similarity image search engine. It's incredibly powerful for finding similar images amongst large image datasets, or just submitting text/natural language queries and finding the most relevant images in your dataset. Really useful tool for introspection into large datasets before annotation or ML work begins. This could potentially be used to filter or downsize your datasets by several orders of magnitude and make annotation and ML work easier and less costly.

Checkout the demo here:

http://ec2-52-39-251-116.us-west-2.compute.amazonaws.com/

And you can checkout our website or email me for updates and email list, etc.:

https://machineperception.co

Assuming you have an NVIDIA GPU, you can build a semantic search engine by indexing CLIP embeds (image or text).

https://github.com/rom1504/clip-retrieval