QDrant-NLP
fiftyone
| QDrant-NLP | fiftyone | |
|---|---|---|
| 1 | 18 | |
| 11 | 6,674 | |
| - | 3.8% | |
| 10.0 | 10.0 | |
| over 1 year ago | 4 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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QDrant-NLP
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Vector Databases for Data-Centric AI (Part 2)
Just clone the repo QDrant-NLP and run: docker-compose up I would like to increase the number of datasets this can be tried on, either with GPU backed lambda functions or by saving many example datasets to S3. So far I've only made a 6K subset of ag_news available. ag_news · Datasets at Hugging Face This is the code snippet used to generate the embeddings via hugging-face:
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?
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Announcing FiftyOne 0.19 with Spaces, In-App Embeddings Visualization, Saved Views, and More!
kalpit-S contributed #2354 – added help link for Mapbox configuration in App
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