autodistill VS material-ui-docs

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

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!

Autodistill

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

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

> 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

I'll be using Material UI for styling the cards. You can install it by visiting the Material UI installation guide.

For the UI, MUI is a huge time saver. It's open-core and thoroughly excellent: https://mui.com/

They also have a lot of pre-built dashboards that tie into various cloud vendors (typically not FOSS though).

(My opinion only, please treat it as just one person's thought process, not some eternal truth)

As a frontend dev, for me it's primarily just an ecosystem thing. There's nothing wrong with HTMX or any other solution, like Ruby on Rails or Hotwire or even other JS frameworks like Angular or Gatsby, but they are not really what I see in the majority of the web dev ecosystem.

By ecosystem, I mean this:

- Developers are easy to find & hire for, and can work on existing code without much training because there are (relatively) standardized practices

- For any common problem, I can easily reuse (or at least learn from the source for) a package on NPM

- For any uncommon problem, I can find multiple robust discussion about it on various forums, Stack, etc. And ChatGPT probably has a workable overview.

- I can reasonably expect medium-term robust vendor support, not just from the framework developers but various hosts, third-party commercial offerings (routers, state management, UI libs, CMSes, etc.), i.e., it's going to stay a viable ecosystem for 3-5 years at least

- I don't have to reinvent the wheel for every new project / client, and can spin up a working prototype in a few minutes using boilerplates and 1-click deploys

I've been building websites since I was a kid some 30 years ago, first using Perl and cgi-bin and then PHP, and evolved my stack with it over time.

I've never been as productive as I am in the modern React ecosystem, especially with Next or Vite + MUI (https://mui.com/). Primarily this is because it allows me to build on top of other people's work and spend time only on the business logic of my app, at a very high level of abstraction (business components) and with a very high likelihood of being able find drop-in solutions for most common needs. I'm not reinventing the wheel constantly, or dealing with low-level constructs like manually updating the DOM. Or worse, dealing with server issues or updating OS packages.

What used to take days/weeks of setup now takes one click and two minutes, and I can have a useable prototype up in 2-3 hours. Because 95%+ of my codebase isn't mine anymore; I can just reuse what someone else built, and then reframe it for my own needs. And when someone else needs to continue the work, they can just pick up where I left off with minimal onboarding, because they probably already have React knowledge.

I think React, for all its faults, has just reached a point of saturation where it's like the old "nobody ever got fired for buying IBM", i.e., it's a safe, proven bet for most use cases. It may or may not be the BEST bet for any project, but it's probably good enough that it would at least warrant consideration, especially if the other stacks have less community/ecosystem support.

Explore Material UI: Material UI Documentation

- UI kit (I personally have good experience with React Material UI - https://mui.com/; there is also https://tanstack.com/)

the network is settled (I got the code from some discussion group). But nothing works. Playwright has also

page.waitForLoadState({ waitUntil: "domcontentloaded" }); etc.

but they are not working for my test cases.

2)

I have noticed that https://mui.com/ have dropdown menus, which implementation is far from normal html option. Mui uses some kind

MUI | Remote UTC-6 to +5 | Multiple roles | Full time | https://mui.com/

I'm a co-founder and the CEO of MUI. Our objective in the short term is to become the UI toolkit for React, unifying the fragmented ecosystem of dependencies into a single set of simple, beautiful, consistent, and accessible React components. In the longer term, our goal is to make building great web UIs quicker, simpler, and accessible to more people through a low-code platform for developers.

Some things we’re proud of:

- 25% of the downloads that React receives.

- 1M developers on our documentation every month.

- Solid financials: profitable

If this sounds interesting to you, we are hiring for: UI Engineers, Product Engineers, Developer Advocate / Content Engineer:

Here, at Woovi, our design system has been wrote using [MUI](https://mui.com/. But, in my opinion, I have some pain points considering how MUI built their components, most focusing on the fact of how they expose their component APIs and how they handle the component structure.

My feeling as a frontend dev was that Material Design You is just run of the mill enshittification at Google. Around the time that came out, Google also started to hide more buttons in the UI, made the drop down shade much more clumsy, got rid of the excellent Pixel fingerprint scanner, etc.

It felt to me like some other busy body design team had to show innovation and so made Material You adopt your wallpaper colors (in some ugly variation). It was like the MySpaceification of Android.

Material Design spawned some of my favorite projects, like MUI: https://mui.com/

That tracks Material v2 (pre you) and IMO is the best web UI currently available. There's some tentative work on adding Material You, but I hope they don't. It's a step backward IMO, form over function and against the original spirit of Material as a usability design library. https://github.com/mui/material-ui/issues/29345

5.material-ui