fiftyone
refinery
fiftyone | refinery | |
---|---|---|
19 | 20 | |
6,712 | 1,365 | |
2.1% | 0.9% | |
10.0 | 4.5 | |
about 17 hours ago | 16 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fiftyone
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May 8, 2024 AI, Machine Learning and Computer Vision Meetup
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.
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Voxel51 Is Hiring AI Researchers and Scientists — What the New Open Science Positions Mean
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.
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How to Estimate Depth from a Single Image
We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.
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How to Cluster Images
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:
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Efficiently Managing and Querying Visual Data With MongoDB Atlas Vector Search and FiftyOne
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.
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FiftyOne Computer Vision Tips and Tricks - March 15, 2024
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.
- FLaNK AI for 11 March 2024
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How to Build a Semantic Search Engine for Emojis
If you want to perform emoji searches locally with the same visual interface, you can do so with the Emoji Search plugin for FiftyOne.
- FLaNK Stack Weekly for 07August2023
- Please don't post like 20 similar images to the art sites?
refinery
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
You definitely forgot https://www.kern.ai/ :)
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How we used AI to automate stock sentiment classification
We will build the web scraper in Kern AI workflow, labeled our news articles in refinery, and then enrich the data with gates AI. After that, we will use workflow again to send out the predictions and the enriched data via a webhook to Slack. If you'd like to follow along or explore these tools on your own, you can join our waitlist here: https://www.kern.ai/
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German's NLP startup Kern AI has raised €2.7M in seed funding to accelerate its recent growth
A platform has been developed by the German startup Kern AI for NLP developers and data scientists to not only control the labeling process but also automate and orchestrate tangential tasks and enable them to address low-quality data that comes their way. Several companies exist substantively to power this labeling process.
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Why and how we started Kern AI (our seed funding announcement)
Fast forward to July ‘22 (after many further product iterations and a full redesign), we open-sourced our product under a new name: Kern AI refinery (the origin of the name is very simple: we want to improve, i.e., refine, the foundation for building models).
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GPT and BERT: A Comparison of Transformer Architectures
Get it for free here: https://github.com/code-kern-ai/refinery
- Open-source tool to label, assess and maintain natural language data. Treat training data like a software artifact!
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Drastically decrease the size of your Docker application
Containers are amazing for building applications. Because they allow you to pack up a programm together with all it's dependencies and execute it wherever you like. That is why our application consists of 20+ individual containers, forming our data-centric IDE for NLP, which you can check out here: https://github.com/code-kern-ai/refinery.
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Introducing bricks, an open-source content-library for NLP
Today we launched bricks, an open-source library which provides enrichments for your natural language processing projects. Our main goal with bricks is to shorten the amount of time that you need from idea to implementation. Bricks also seamlessly integrates into our main tool, the Kern AI refinery.
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How to fine-tune your embeddings for better similarity search
This blog post will share our experience with fine-tuning sentence embeddings on a commonly available dataset using similarity learning. We additionally explore how this could benefit the labeling workflow in the Kern AI refinery. To understand this post, you should know what embeddings are and how they are generated. A rough idea of what fine-tuning is also helps. All the code and data referenced in this post is available on GitHub.
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Build for Hugging Face, Rasa or Sklearn
We've built our open-source IDE for data-centric NLP with the belief that data scientists and engineers know best what kind of framework they want to use for their model building. Today, we'll show you three new adapters for the SDK.
What are some alternatives?
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
sqlx - 🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
dbs-tools - Perl tools to transform account / transaction data from DBS Bank into proper CSV
ZnTrack - Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
azuredatastudio - Azure Data Studio is a data management and development tool with connectivity to popular cloud and on-premises databases. Azure Data Studio supports Windows, macOS, and Linux, with immediate capability to connect to Azure SQL and SQL Server. Browse the extension library for more database support options including MySQL, PostgreSQL, and MongoDB.
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
serde_postgres - Easily Deserialize Postgres rows.
streamlit - Streamlit — A faster way to build and share data apps.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.