supervision VS oil

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

Some related references (on a somewhat messy wiki page) - https://github.com/oilshell/oil/wiki/Compact-AST-Representat...

Feel free to add others

will prevent almost all of the "silent footguns".

YSH has strict:all and then a bunch of NEW features.

There's been good feedback recently, which has led to many concrete changes. So your experience can definitely influence the language! https://github.com/oilshell/oil/wiki/Where-To-Send-Feedback

Regarding writing tools/tests/benchmarks in bash+Python, vs. writing tools in your main language:

I think we might eventually concede that something Debian-like is the “standard development environment” (at least for server side stuff, i.e. not iOS apps)

In this case, bash+Python is a non-issue. It works extremely reliably. That’s actually why I use it! Everything else seems to break, or it’s really slow (node.js is a very common alternative).

- Microsoft conceded this back in ~2017, by building Linux into their kernel with WSL, and providing Ubuntu on top

Yes bash + Python is a disaster on Windows (I have scars from it), but Microsoft agrees that the right place to solve that is in Windows :-)

- Every CI system runs Debian/Ubuntu

- Every hosting provider runs Debian/Ubuntu

- Every online dev env like gitpod.io provides Debian/Ubuntu

This is somewhat related to remote dev envs: https://lobste.rs/s/ucirlx/lapdev_self_hosted_remote_dev

One vision for https://www.oilshell.org/ is that the CI environment is the dev environment is the hosting environment.

Everything is just an equal node in a distributed system. BUT it’s more git like, in that you explicitly sync and work “locally”, wherever that is. You don’t have the network chatter and flakiness of “the cloud”.

Oils has a very large set of monotonically increasing properties too - https://www.oilshell.org/release/0.21.0/quality.html

All that is bash+Python that is run on every commit, and it’s extremely good at catching bugs and perf regressions.

I’m skeptical that any project has that level of quality automation written in pure Rust or Zig. More likely it’s a bunch of cloud services with YAML.

Also a bunch of “hard-coded” toolchains that you can’t script with bespoke code. Like some shell commands in your package.json, which is just a worse way of writing a shell script.

Our quality process is all self-hosted, in the repo, and runs on both Github Actions and sourcehut - https://www.oilshell.org/release/0.21.0/pub/metrics.wwz/line...

bash and Python runs perfectly on Github Actions and sourcehut, with zero change. Containers also do.

(Although we need to unify the CI and release, because the release runs on 2 different real hardware machines, while CI is cloud only.)

Also, a main point Oils is that bash now has another highly compatible, spec-driven implementation – OSH. Having 2 independent implementations is something newer languages don’t have.

(copy of lobste.rs comment)

in your bashrc to enable it. I've used it for probably ~18 years now.

It also works with https://www.oilshell.org/ since we use GNU readline. Just 'set -o vi' in ~/.config/oils/oshrc

No other shell does that.

But I didn't know it was called MULTIOS until now. (I guess that's read "mult I/O's"? I have a hard time not reading it was multi-OS :) )

It seems a bit niche to be honest, but it's possible to support in Oils.

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Oils also uses Unix domain sockets already for the headless shell protocol

https://github.com/oilshell/oil/wiki/Headless-Mode

We could do something like dgsh, but so far I haven't seen a lot of uptake / demand. Every time it's mentioned, somebody kinda wants it, and then it kinda peters out again ... still possible though.

I think flat files work fine for a lot of use cases, and once you add streaming, you also want monitoring, more control over backpressure/queue sizes, etc.

which works well. You don't have to clean when rebuilding variants. IMO this is 100% essential for writing C++ these days. You need a bunch of test binaries, and all tests should be run with ASAN and UBSAN.

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I wrote a mini-bazel on top of Ninja with these features:

https://www.oilshell.org/blog/2022/10/garbage-collector.html...

So it's ~1700 lines, but for that you get the build macros like asdl_library() generating C++ and Python (the same as proto_library(), a schema language that generates code)

And it also correctly finds dependencies of code generators. So if you change a .py file that is imported by another .py file that is used to generated a C++ header, everything will work. That was one of the trickier bits, with Ninja implicit dependencies.

I also use the Bazel-target syntax like //core/process

This build file example mixes low level Ninja n.rule() and n.build() with high level r.cc_library() and so forth. I find this layering really does make it scale better for bigger projects

https://github.com/oilshell/oil/blob/master/asdl/NINJA_subgr...

Some more description - https://lobste.rs/s/qnb7xt/ninja_is_enough_build_system#c_tu...

This is sort of a category error...

re2c is a lexer generator, and YAML and Python are recursive/nested formats.

You can definitely use re2c to lex them, but it's not the whole solution.

I use it for everything possible in https://www.oilshell.org, and it's amazing. It really reduces the amount of fiddly C code you need to parse languages, and it drops in anywhere.

SEEKING VOLUNTEERS - https://www.oilshell.org/ - https://github.com/oilshell/oil/

I'm looking for people to help fill out the "standard library" for Oils/YSH. We're implementing a shell for Python and JavaScript programmers who avoid shell!

On the surface, this is writing some very simple functions in typed Python. But I've realized that the hardest parts are specifying, TESTING, and documenting what the functions do.

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The most recent release announcement also asks for help - https://www.oilshell.org/blog/2024/01/release-0.19.0.html (long)

If you find all those details interesting (if maybe overwhelming), you might have a mind for language design, and could be a good person to help.

Surveying what Python and JavaScript do is very helpful, e.g. for the recent Str.replace() function, which is nontrivial (takes a regex or string, replacement template or string)

But there are also very simple methods to get started, like Dict.values() and List.indexOf(). Other people have already contributed code. Examples:

https://github.com/oilshell/oil/commit/58d847008427dba2e60fe...

https://github.com/oilshell/oil/commit/8f38ee36d01162593e935...

This can also be useful to tell if you'll have fun working on the project - https://github.com/oilshell/oil/wiki/Where-Contributors-Have...

More on #help-wanted on Zulip (requires login) - https://oilshell.zulipchat.com/#narrow/stream/417617-help-wa...

Please send a message on Github or Zulip! Or e-mail me andy at oilshell dot org.

This is true, but then the corrolary is that new PRs need to come with this higher and rigorous level of test coverage.

And then that becomes a bit of a barrier to contribution -- that's a harness

I often write entirely new test harnesses for features, e.g. for https://www.oilshell.org, many of them linked here . All of these run in the CI - https://www.oilshell.org/release/latest/quality.html

The good thing is that it definitely helps me accept PRs faster. Current contributors are good at this kind of exhaustive testing, but many PRs aren't