fiftyone VS ZnTrack

My experience has been much like this. For twenty years, I’ve emphasized scientific and engineering discovery in my work as an academic researcher, publishing these findings at the top conferences in computer vision, AI, and related fields. Yet, at my company, we focus on infrastructure that enables others to unlock scientific discovery. We have built a software framework that enables its users to do better work when training models and curating datasets with large unstructured, visual data — it’s kind of like a PyTorch++ or a Snowflake for unstructured data. This software stack, called FiftyOne in its single-user open source incarnation and FiftyOne Teams in its collaborative enterprise version, has garnered millions of installations and a vibrant user community.

We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.

With all that background out of the way, let’s turn theory into practice and learn how to use clustering to structure our unstructured data. We’ll be leveraging two open-source machine learning libraries: scikit-learn, which comes pre-packaged with implementations of most common clustering algorithms, and fiftyone, which streamlines the management and visualization of unstructured data:

FiftyOne is the leading open-source toolkit for the curation and visualization of unstructured data, built on top of MongoDB. It leverages the non-relational nature of MongoDB to provide an intuitive interface for working with datasets consisting of images, videos, point clouds, PDFs, and more.

Welcome to our weekly FiftyOne tips and tricks blog where we recap interesting questions and answers that have recently popped up on Slack, GitHub, Stack Overflow, and Reddit.

If you want to perform emoji searches locally with the same visual interface, you can do so with the Emoji Search plugin for FiftyOne.

kalpit-S contributed #2354 – added help link for Mapbox configuration in App

I expaned a bit on them with my own package https://zntrack.readthedocs.io/ - a general framework for building DVC pipelines through python scripts (and more). This finally brings me to the project I'm actually working on https://github.com/zincware/IPSuite which brings all of this together for the specific use case of machine learned interatomic potentials.

This looks really interesting! I have a similar scenario but haven't looked into it yet. Have you looked at dvc.org - I'm planning on using it together with slurm and what they call CML for my projects. On that context I also wrote a tool that makes DVC more pythonic https://github.com/zincware/ZnTrack altough I'm currently restructuring it a bit but having backwards compatibility in mind.