supervision VS fish-shell

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

Reminds of the note at the bottom of Fish's releases. It's there because the build system cannot determine the current version for some reason. Hopefully that will go away now that they have switched to a different language / build system. The custom tarball is used by Arch Linux at the very least.

https://github.com/fish-shell/fish-shell/releases/tag/3.7.1

https://github.com/fish-shell/fish-shell/issues/7772#issueco...

https://gitlab.archlinux.org/archlinux/packaging/packages/fi...

A recent practical example of the former: the fish shell re-wrote incrementally from C++ to Rust, and is almost finished https://github.com/fish-shell/fish-shell/discussions/10123

An example of the latter: c2rust, which is a work in progress but is very impressive https://github.com/immunant/c2rust

It currently translates into unsafe Rust, but the strategy is to separate the "compile C to unsafe Rust" steps and the "compile unsafe Rust to safe Rust" steps. As I see it, as it makes the overall task simpler, allows for more user freedom, and makes the latter potentially useful even for non-transpiled code. https://immunant.com/blog/2023/03/lifting/

And this discussion from November has an update on the progress: https://github.com/fish-shell/fish-shell/discussions/10123

We're using bash as our terminal shell for now (it is standard in many distros) but it is not the only one out there. If you want to test out zsh, fish or oh-my-zsh, you will see that there are a few differences and the features are usually the main differentiator. Try that, poke around.

They have a variety of reasons to move to rust, as outlined in their original rust discussion[1]. Mostly around finding other contributors, and adding an async/parallel mode they're comfortable with.

[1] https://github.com/fish-shell/fish-shell/pull/9512