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autodistill
reframe | autodistill | |
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7 | 13 | |
156 | 1,552 | |
- | 5.3% | |
0.0 | 9.2 | |
8 months ago | about 1 month 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.
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- Custom domains for Airtable
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Ask HN: Who is hiring? (August 2023)
NNext.ai » Software Engineer » Python » Full-time » NY, NY, USA » Remote
At [NNext.ai](http://NNext.ai) (read “Next.ai”), we are building building core infrastructure to simplify the process of building, deploying, and managing LLM-powered AI agents on tabular data, making it the most efficient solution available. NNext AI enables users to combine the power of Large Language Models (LLMs) like GPT with tools like the browser and Google Search, greatly increasing your productivity on workflows you once spent countless hours on. We are building a framework specialized for tabular data such as Spread sheets, Database Tables and CSVs, thereby taking advantage of the similarities and co-dependencies amongst data.
We are lean team of engineers looking for our first full time OSS contributor. Desirable experience includes distributed systems working with tools like Celery, Dash and Redis Streams. Visibility on Github is a huge plus. Show us previous OSS software you’ve contributed to or public personal projects with significant usage.
Website: https://nnext.ai
Github: https://github.com/nnextai/aigent
Email: [email protected]
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[P] NNextDB (read: “nextdb”), an open-source, blazingly fast ⚡️, vector search database to power your AI apps 🦾.
https://github.com/nnextdb/nnext/blob/main/FAQs.md Q: How does this differ from Faiss, ScaNN and Annoy?
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NNextDB (read: “nextdb”), an open-source, blazingly fast ⚡️, vector search database to power your AI apps 🦾.
What’s NNext? [Say “Next”] NNextDB is a blazingly fast ⚡️, open-source 📖, (vector) neural search 🔎 engine for building delightful AI Apps 🦾. Github: https://github.com/nnextdb/nnext Like what you see? Please give us a Star 🤩. Applications Vector search is a key concept in modern machine learning systems. It’s the technology behind
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[D] Doing similarity search for images?
These things mostly work by having some vision model, chopping it off somewhere in the middle and using that layer as an "embedding" for your image. Then you look for images that have embeddings which are close together (normally in the sense of Euclidean distance, e.g.)
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[D] What features would you like to see in a vector search database?
Code Link: https://github.com/nnextdb/nnext
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Ask HN: What Are You Working On? (August 2022)
NNextDB - a blazing fast, open source, vector search database for building AP applications.
https://github.com/nnextdb/nnext
autodistill
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Ask HN: Who is hiring? (February 2024)
Roboflow | Open Source Software Engineer, Web Designer / Developer, and more. | Full-time (Remote, SF, NYC) | https://roboflow.com/careers?ref=whoishiring0224
Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.
Over 250k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. We now host the largest collection of open source computer vision datasets and pre-trained models[2]. We are pushing forward the CV ecosystem with open source projects like Autodistill[3] and Supervision[4]. And we've built one of the most comprehensive resources for software engineers to learn to use computer vision with our popular blog[5] and YouTube channel[6].
We have several openings available but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. Our engineering culture is built on a foundation of autonomy & we don't consider an engineer fully ramped until they can "choose their own loss function". At Roboflow, engineers aren't just responsible for building things but also for helping us figure out what we should build next. We're builders & problem solvers; not just coders. (For this reason we also especially love hiring past and future founders.)
We're currently hiring full-stack engineers for our ML and web platform teams, a web developer to bridge our product and marketing teams, several technical roles on the sales & field engineering teams, and our first applied machine learning researcher to help push forward the state of the art in computer vision.
[1]: https://roboflow.com/?ref=whoishiring0224
[2]: https://roboflow.com/universe?ref=whoishiring0224
[3]: https://github.com/autodistill/autodistill
[4]: https://github.com/roboflow/supervision
[5]: https://blog.roboflow.com/?ref=whoishiring0224
[6]: https://www.youtube.com/@Roboflow
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Is supervised learning dead for computer vision?
The places in which a vision model is deployed are different than that of a language model.
A vision model may be deployed on cameras without an internet connection, with data retrieved later; a vision model may be used on camera streams in a factory; sports broadcasts on which you need low latency. In many cases, real-time -- or close to real-time -- performance is needed.
Fine-tuned models can deliver the requisite performance for vision tasks with relatively low computational power compared to the LLM equivalent. The weights are small relative to LLM weights.
LLMs are often deployed via API. This is practical for some vision applications (i.e. bulk processing), but for many use cases not being able to run on the edge is a dealbreaker.
Foundation models certainly have a place.
CLIP, for example, works fast, and may be used for a task like classification on videos. Where I see opportunity right now is in using foundation models to train fine-tuned models. The foundation model acts as an automatic labeling tool, then you can use that model to get your dataset. (Disclosure: I co-maintain a Python package that lets you do this, Autodistill -- https://github.com/autodistill/autodistill).
SAM (segmentation), CLIP (embeddings, classification), Grounding DINO (zero-shot object detection) in particular have a myriad of use cases, one of which is automated labeling.
I'm looking forward to seeing foundation models improve for all the opportunities that will bring!
- Ask HN: Who is hiring? (October 2023)
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Autodistill: A new way to create CV models
Autodistill
- Show HN: Autodistill, automated image labeling with foundation vision models
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Show HN: Pip install inference, open source computer vision deployment
Thanks for the suggestion! Definitely agree, we’ve seen that work extremely well for Supervision[1] and Autodistill, some of our other open source projects.
There’s still a lot of polish like this we need to do; we’ve spent most of our effort cleaning up the code and documentation to prep for open sourcing the repo.
Next step is improving the usability of the pip pathway (that interface was just added; the http server was all we had for internal use). Then we’re going to focus on improving the content and expanding the models it supports.
[1] https://github.com/roboflow/supervision
[2] https://github.com/autodistill/autodistill
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Ask HN: Who is hiring? (August 2023)
Roboflow | Multiple Roles | Full-time (Remote, SF, NYC) | https://roboflow.com/careers?ref=whoishiring0823
Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.
Over 250k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. We now host the largest collection of open source computer vision datasets and pre-trained models[2]. We are pushing forward the CV ecosystem with open source projects like Autodistill[3] and Supervision[4]. And we've built one of the most comprehensive resources for software engineers to learn to use computer vision with our popular blog[5] and YouTube channel[6].
We have several openings available, but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. Our engineering culture is built on a foundation of autonomy & we don't consider an engineer fully ramped until they can "choose their own loss function". At Roboflow, engineers aren't just responsible for building things but also for helping figure out what we should build next. We're builders & problem solvers; not just coders. (For this reason we also especially love hiring past and future founders.)
We're currently hiring full-stack engineers for our ML and web platform teams, a web developer to bridge our product and marketing teams, several technical roles on the sales & field engineering teams, and our first applied machine learning researcher to help push forward the state of the art in computer vision.
[1]: https://roboflow.com/?ref=whoishiring0823
[2]: https://roboflow.com/universe?ref=whoishiring0823
[3]: https://github.com/autodistill/autodistill
[4]: https://github.com/roboflow/supervision
[5]: https://blog.roboflow.com/?ref=whoishiring0823
[6]: https://www.youtube.com/@Roboflow
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AI That Teaches Other AI
> Their SKILL tool involves a set of algorithms that make the process go much faster, they said, because the agents learn at the same time in parallel. Their research showed if 102 agents each learn one task and then share, the amount of time needed is reduced by a factor of 101.5 after accounting for the necessary communications and knowledge consolidation among agents.
This is a really interesting idea. It's like the reverse of knowledge distillation (which I've been thinking about a lot[1]) where you have one giant model that knows a lot about a lot & you use that model to train smaller, faster models that know a lot about a little.
Instead, you if you could train a lot of models that know a lot about a little (which is a lot less computationally intensive because the problem space is so confined) and combine them into a generalized model, that'd be hugely beneficial.
Unfortunately, after a bit of digging into the paper & Github repo[2], this doesn't seem to be what's happening at all.
> The code will learn 102 small and separte heads(either a linear head or a linear head with a task bias) for each tasks respectively in order. This step can be parallized on multiple GPUS with one task per GPU. The heads will be saved in the weight folder. After that, the code will learn a task mapper(Either using GMMC or Mahalanobis) to distinguish image task-wisely. Then, all images will be evaluated in the same time without a task label.
So the knowledge isn't being combined (and the agents aren't learning from each other) into a generalized model. They're just training a bunch of independent models for specific tasks & adding a model-selection step that maps an image to the most relevant "expert". My guess is you could do the same thing using CLIP vectors as the routing method to supervised models trained on specific datasets (we found that datasets largely live in distinct regions of CLIP-space[3]).
[1] https://github.com/autodistill/autodistill
[2] https://github.com/gyhandy/Shared-Knowledge-Lifelong-Learnin...
[3] https://www.rf100.org
- Autodistill: Use foundation vision models to train smaller, supervised models
- Autodistill: use big slow foundation models to train small fast supervised models (r/MachineLearning)
What are some alternatives?
skeleton - A fully featured UI toolkit for Svelte + Tailwind. [Moved to: https://github.com/skeletonlabs/skeleton]
anylabeling - Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!!
vector-search-compilation - A compilation of Vector Search Databases
tabby - Self-hosted AI coding assistant
open-recipe-project - Free, and open recipes for anyone to use
Shared-Knowledge-Lifelong-Learnin
needle - A CLI tool that finds a needle (opening/intro and ending/credits) in a haystack (TV or anime episode).
segment-geospatial - A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
filterbox - Index your Python objects for fast lookup by their attributes. [Moved to: https://github.com/manimino/ducks]
opentofu - OpenTofu lets you declaratively manage your cloud infrastructure.
pyroscope-rs - Pyroscope Profiler for Rust. Profile your Rust applications.
supervision - We write your reusable computer vision tools. 💜