supervision VS bun

You can always slice the images into smaller ones, run detection on each tile, and combine results. Supervision has a utility for this - https://supervision.roboflow.com/latest/detection/tools/infe..., but it only works with detections. You can get a much more accurate result this way. Here is some side-by-side comparison: https://github.com/roboflow/supervision/releases/tag/0.14.0.

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

You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)

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

a new, popular library for basic functionality (converting between annotation formats, evaluating models, doing object tracking) has been supervision: https://github.com/roboflow/supervision

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

repository: https://github.com/roboflow/supervision documentation: https://roboflow.github.io/supervision

It has a decent compatibility with both Jest and Vitest's APIs (you can track progress here so you can use it as almost a drop-in replacement for either. Just as Node's, it has describe/it, mock, test and others, but with the expect syntax (which I find more readable). For example:

In this third and final article in the series on HTML Streaming, we will explore the practical implementation of the Diff DOM Streaming library in web browsing. This approach will allow any website using web components to retain its state during browsing. We will discuss in detail how to achieve this step by step using VanillaJS and Bun.

At Node Conference 2023, Jarred Sumner (creator of Bun) showed a demo of server components in Bun, so there is at least partial support in that ecosystem. The Bun repo provides bun-plugin-server-components as the official plugin for server components. And while I haven’t looked at it in-depth, Marz claims to be a “React Server Components Framework for Bun”.

Continuously evolving, Bun is currently optimized for MacOS and Linux, with ongoing efforts towards Windows compatibility. Tailored for resource-constrained environments like serverless functions, it emerges as an ideal solution. The Bun team is committed to achieving comprehensive Node.js compatibility and seamless integration with prevalent frameworks. For those intrigued by Bun's potential and want to give it a try, more information is available on its website at https://bun.sh/.

Let’s say you are interested in learning more about Bun and probably give it a try. Bun has a website, where you can learn more about Bun and its features (including all the benchmark data captured in this issue), and here is the link.

Looks like it, it seems the 2% are mostly odd platform specific issues that the authors' did not deem very important (my assumption for the release happening anyway). AFAIK this[1] PR tries to fix them.

[1]: https://github.com/oven-sh/bun/pull/9729

Bun has a solution for it. First of all, it already has a list of trusted dependencies. For them, Bun will execute all necessary scripts by default. Otherwise, you can add it to trustedDependecies in your package.json file. In Bun community usage of trustedDependencies is a hot topic. There are several suggestions on how to improve it.

Install Node.js (or Bun, or Deno, or whatever JS runtime you prefer) if it's not there

I think maybe I was unclear. I'm talking about writing libraries that abstract across these differences and provide a single API, as sibling describes. I already know it's possible. I made a simple filesystem abstraction here[0] and a very simple HTTP library that uses it here[1]. They both work in Node/Deno and the browser. Unfortunately I ran into issues with Bun's slice implementation[2]. But I suspect there's a much better way of detecting and using the different backends.

[0]: https://github.com/waygate-io/fs-js

[1]: https://github.com/waygate-io/http-js

[2]: https://github.com/oven-sh/bun/issues/7057