fiftyone VS Serpent.AI

In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.

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.

A: I am. While serpent.ai attempted to get an AI to play Isaac, the project hasn't been updated in years.

You don't need a whole team nowadays. Amazon has services that let you train your own neural nets with a little bit of knowledge. Then there are tools like SerpentAI that let your AI interface with games (don't know if it works with Blizzard games, but it works with Steam).

i cant. is there any way https://github.com/SerpentAI/SerpentAI would work. the game is entirely mouse movements.

Hey! So, I'd like to implement NEAT machine learning to train an agent to play Donkey Kong Country, but there doesn't seem to be much in the way of tutorials/examples for Serpent.AI (like, its weirdly dead given how powerful it seems to be and github page is full of dead links) so I wanted to see if any of you fine folk would recommend for/against its use or that of an alternative. Any other advice also appreciated.

Serpent AI : Game Agent Framework. Helping you create AIs / Bots to play any game you own! BETA