DearPy3D VS skweak

Dear PyGui is awesome and supports creating node editors. 3D is not really supported yet (although matrix functions are), but future versions will support 3D. The core developers are very much interested in 3D rendering. As a little test, Hoffstadt created DearPy3D. He is currently working on Pilotlight, which is still early stages and eventually will be the core of Dear PyGui version 3.

Just a quick update since then is that Dear PyGui has reached version 1.0 and the API is now stable with a proper deprecation policy. Additional features include support for extremely dynamic tables, became faster still, introduction of the first steps into 3D and drawing transformations, support for multiple fonts, node editor and many small improvements and bug fixes. There are still many ideas for future development, including more 3D.

With regard to 3D, are you aware of DearPy3D by the same developers (still under development, also available under the MIT license)?

hoffstadt/Marvel: Dear PyGui 3D Engine (early development) (github.com)

Thanks for pointing me in the right direction. Seems like there’s a few other approaches with weak supervision: https://github.com/NorskRegnesentral/skweak

Code for https://arxiv.org/abs/2104.09683 found: https://github.com/NorskRegnesentral/skweak

Hi Raza here, one of the other co-founders.

I know that HN likes to nerd out over technical details so thought I’d share a bit more on how we aggregate the noisy labels to clean them up.

At the moment we use the great Skweak [1] open source library to do this. Skweak uses an HMM to infer the most likely unobserved label given the evidence of the votes from each of the labelling functions.

This whole strategy of first training a label model and then training a neural net was pioneered by Snorkel. We’ve used this approach for now but we actually think there are big opportunities for improvement.

We’re working on an end-to-end approach that de-noises the labelling function and trains the model at the same time. So far we’ve seen improvements on the standard benchmarks [2] and are planning to submit to Neurips.

R

[1]: Skweak package: https://github.com/NorskRegnesentral/skweak

skweak.

For more advanced unsupervised labeling, you should check skweak

I'm the main developer behind skweak by the way, happy to hear you're interested in our toolkit :-) We do already have a small list of products (see https://github.com/NorskRegnesentral/skweak/blob/main/data/products.json) extracted from DBPedia and Wikidata, but it may not be exactly the type of products you're looking for.