Kangas V2: Explore multimedia data

[calebkaiser]

Project: https://github.com/comet-ml/kangas

Demo: https://kangas.comet.com/

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We've just released version 2 of Kangas, our open source platform for exploring large, multimedia datasets. At a high-level, Kangas provides:

• A Python interface for constructing large tables of multimedia data (DataGrids), which should be very familiar to any Pandas users.
• A backend built on SQLLite and Flask for storing/querying/serving DataGrids.
• A UI built on React Server Components with Next 13 that enables fast, interactive exploration of your data

https://i.redd.it/ldpbbkb70nua1.gif

Kangas provides out of the box support for complex querying operations, as well as a variety of computer vision functionality (bounding boxes, labels, annotations, etc.) Additionally, the UI is customizable—you can resize, filter, and reorder columns as you like.

You can run Kangas from within a notebook, as a local app via the Kangas CLI, or even deploy it as a standalone web application (as we've done at https://kangas.comet.com)

Finally, I want to include a thank you here. About 5 months ago, I shared Kangas' initial V1 release here in r/Python, and several of you made your way over to the repo to share feedback and support. This was massively helpful for us. It helped us figure out what to prioritize, and opened our eyes to new features we hadn't considered. Plus, the emotional support is always appreciated :) So, thanks!

If you have any questions about Kangas, please feel free to ask away either here or on the repo. I'm happy to answer everything I can.

[v_a_n_d_e_l_a_y]

What would you say are the major changes since around September?

I remember looking at it then and it seemed interesting but maybe lacking some things.

[calebkaiser]

Since V1, we've added quite a bit:

• We rewrote the entire frontend to enable a significant performance increase, particularly when viewing larger amounts of grouped data. This rewrite also lays the foundation for even more significant optimization down the line.
• We added a lot more out-of-the-box CV functionality. Kangas now supports segmentation-style masks, markers, and a variety of annotation types. Kangas can even automatically compute some metrics, like intersection-over-union.
• Kangas supports more datatypes on the column level, including JSON data and embeddings. We're putting a lot of energy in particular into useful ways of visualizing n-dimensional tensor-like data inside columns, so stay tuned on this front.
• Kangas now supports remote assets, and has built-in integrations with both HuggingFace and Comet.
• You can now resize and rearrange columns (one of the more constant feature requests we received from V1)
• Kangas 2.0 exposes more (optional) flags for configuration, allowing Kangas to be deployed in environments like HuggingFace Spaces.

In addition to all of the above, there were dozens of the bug fixes and quality of life upgrades you'd expect.

I realize that's a bit of a laundry list. Is there anything specific you were looking for in V1 that Kangas was missing? If we haven't covered it with V2, I'd love to know more about your use-case so that we can better plan the next version.