TACO VS revery

http://tacodataset.org/ <- Open source dataset if you want to train a classifier, I like this one

Hi, So my project is to do with object detection on trash in the wild on this fairly obscure dataset: http://tacodataset.org/ and I was thinking of applying vision transformers to it for feature extraction. I was thinking of taking the YOLOX implementation and swapping out the backbone with swin transformers and perform bunch of comparisons/experiments for the write up. Sort of like how they applied swin transformers to mask R-CNN here but I am struggling to understand where to begin.

I was wondering if anyone had a script or could point me to one that would be able to convert polygons from image segmentation to bounding boxes for object detection. I am looking to create a trash detector to run on my trash picking up robot. I found the TACO dataset, but it uses segmentation and I just want to start with bounding boxes. Any help would be appreciated.

OCaml has actually put some decent effort into good GUI libraries, such as https://github.com/revery-ui/revery.

I never tried CoffeeScript since nobody pays me for it, though I am curious about ReasonML as an alternative, there's a Neovim front-end[0] coded in Reason that compiles natively[1], and supports existing VS Code plugins from the VSCodium plugin repository[2] which I still have yet to look at how the heck they pulled that bit off, but it is pretty interesting.

[0]: https://github.com/onivim/oni2#introduction

[1]: https://github.com/revery-ui/revery

[2]: https://open-vsx.org/

Maybe a technology like https://www.outrunlabs.com/revery/ would provide a better experience though it would require rebuilding the frontend, I presume.

It uses Revery which is still just javascript

No, I can’t say I’m familiar with a mature, cross-platform GUI framework which exists today that is any good. I’m keeping an eye on Revery, though.