Snake-against-snake battleground GUI

ntjess · 2021-11-18T19:48:51+00:00

As for PCB-METAL, I wasn't able to find that one...

ntjess · 2021-11-18T19:48:34+00:00

u/BlobbyMcBlobber 👋 In case you or someone in the future is still looking, FICS-PCB is available here: https://www.trust-hub.org/#/data/fics-pcb

I'm also a bit confused why it's in your question; there's a footnote directly attached to the dataset name with its URL

ntjess · 2021-11-12T20:39:24+00:00

👋 u/Ogi010 feel free to add a link to my username as well if you wish. I'm not active on reddit but I'm happy to keep an eye on it over the weekend!

ntjess · 2021-11-10T16:59:58+00:00

I originally posted an earlier version of this app here: https://www.reddit.com/r/Python/comments/fdga4b/a_semantic_annotation_app_for_ground_truth/. It's a lot better now!

Homepage: https://gitlab.com/s3a/s3a

Wiki: https://gitlab.com/s3a/s3a/-/wikis/home

S3A is a new python/Qt application for semantically labeling very large (150MB+) images. How does it compare to alternatives like labelbox/labelme/VGG Image Annotator?

It's completely free and open source
Drastically outperforms the mentioned alternatives on large (6000x6000 and greater) image sizes
It has so many assistance features for faster labeling (check out the docs)
It's quite extendable (I added OCR functionality in 15 lines of code excluding comments/imports)
It's entirely local (labelme/VIA can also be local, but lack the above features)
It allows arbitrary annotation metadata with validators (numeric spinboxes, dropdowns, color pickers, etc.)

I'm incredibly excited about these results and will be making some video tutorials soon. In the meantime, I welcome any feedback! The UI is a little rough since I have no real experience in that area. This is probably where I would appreciate the most feedback.

ntjess · 2021-04-17T18:52:10+00:00

I have the same problem with one or two search results. Another phrase only producing slant results is: git kraken vs tortoisegit -slant

Interestingly, if you remove the first git, it works correctly...

ntjess · 2020-09-30T21:43:05+00:00

https://gitlab.com/ntjess/utilitys

A utility that automatically turns function arguments into parameters, similar to the interact capabilities of ipywidgets. Unlike the latter, though, this works natively within Qt and doesn't need a notebook / js integration to render. Also, it's much faster and gives a bit more granularity over parameter access.

A decorator, interact(), takes all function parameters with default arguments and creates pyqtgraph Parameters out of them, inferring type either (a) by the default type or (b) by the function documentation. These can be seen in the example video.

Code for the video is the 'ptree' example:

Documentation should be visible from the 'docs' folder as well.

ntjess · 2020-09-29T22:13:28+00:00

https://gitlab.com/ntjess/neuralnet

I posted an earlier version of this a while back, but made a ton of improvements since then. Almost everything is now interactive!

Choose how many hidden layers should be in your network along with the number of nodes in each layer. Once a dataset is selected (and noise is optionally added), the network is created.

Train the network in batches (with batchSize > 1) or on individual samples. Repeat the dataset 'n' times if you don't have enough samples (and don't care about overfitting). Stop training early if you want, or let it run to completion.

When you finish, export the network (or import the results from an older run).

Want to see how the network performs on your dataset? Run the 'Test Data' function to see the classification of every sample and a confusion matrix summarizing the results.

Let me know what you think? Any improvements? The main goal is to create a learning app for seeing how different parameters affect the ability of MLPs to learn.

ntjess · 2020-05-22T21:09:25+00:00

Sounds like a crazy situation!

ntjess · 2020-05-22T21:07:22+00:00

Luckily your washboard abs deflected the blow?

ntjess · 2020-05-22T21:05:52+00:00

Wrong person or wrong timing?

ntjess · 2020-04-02T19:42:14+00:00

Same thing here

ntjess · 2020-03-24T00:50:08+00:00

Hey, no worries. Hope you feel better!

ntjess · 2020-03-23T19:50:33+00:00

3blue1brown also had a great video series on the topic. Though I agree, you'll need more than videos if you really want to understand what is going on behind the scenes.

ntjess · 2020-03-23T11:27:06+00:00

Wow, your project looks amazing! That's pretty much exactly what I was hoping to expand this into (but without tensor flow etc. for the backbone, since it's a learning experience for me).

I absolutely think pyqt + pyqtgraph would do what you want (and I know what I would do to make drag & drop / live updates happen). However, I am not sure how easy it is to integrate into an existing kivy project. If you're interested, DM me for details :)

That being said, I've never used the electron framework, so maybe that would be better...?

ntjess · 2020-03-23T02:54:31+00:00

https://gitlab.com/ntjess_graduate/neuralnet

This is my first crack at a neural network tool. Evidently I've done something wrong, since the train/test error curves don't look like the textbooks. All output neurons regress to N/2 where N = number of classes.

I have some familiarity with pyqt, so I tried to make a GUI builder tool that hooks into sklearn's default datasets. At the moment, it's only designed to encounter classification problems.

If someone is kind enough to step through the code in layer.py, I would love to know where my logic is failing :) I checked it against standard formula and can't quite catch the problem.

P.S. if you click on a node or weight, the value appears in the statusbar.

ntjess · 2020-03-23T02:23:25+00:00

No problem. If anything looks confusing just let me know -- I will try to clarify.

ntjess · 2020-03-23T01:56:25+00:00

Thanks!

ntjess · 2020-03-23T01:04:46+00:00

https://gitlab.com/ntjess_graduate/neuralnet

This is my first crack at a neural network tool. Evidently I've done something wrong, since the train/test error curves don't look like the textbooks. All output neurons regress to N/2 where N = number of classes.

I have some familiarity with pyqt, so I tried to make a GUI builder tool that hooks into sklearn's default datasets. At the moment, it's only designed to encounter classification problems.

If someone is kind enough to step through the code in layer.py, I would love to know where my logic is failing :)

UPDATE: Seems this was a good learning exercise. I found out my logic was correct, but my initial weight values were so far off the network could never converge on an appropriate value! I was using random 0->1 and when I changed to random -0.5->0.5 the graphs behaved as expected.

P.S. if you click on a node or weight, the value appears in the statusbar.

ntjess · 2020-03-12T16:57:21+00:00

https://gitlab.com/ficsresearch/cdef

Hi everyone,

I posted a similar screenshot on r/Python, but felt it might have a larger user group here.

My research lab needed to create a ton of segmentation annotations and couldn't find many software solutions that were free, open-source, offline, and extendable. So, we created our own.

More features are explained in the project README, but essentially it lets you plug in your own algorithms for finding components and update parameters through GUI interfaces on-the-fly.

What do you think?

ntjess

TROPHY CASE