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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-24 | 312 |
| 2026-07-25 | 312 |
| 2026-07-28 | 312 |
| 2026-07-30 | 312 |
| 2026-08-06 | 312 |
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<h1>Update 2</h1> Updated documentation for using Tensorflow-directml on windows for broad support on any modern gpu with sufficient memory. <hr> <h1>Update</h1> Pushed a new implementation os starnet in TF2.x. The whole implementation is in one file *starnet_v1_TF2.py*. I also created a few Jupyter notebooks for ease of use: 1. starnet_v1_TF2_transform.ipynb - loads and transforms an image. 2. starnet_v1_TF2.ipynb - more detailed example that loads a model and shows how to train it (really simple as well I think). Weights for the new model can be found <a href="https://www.dropbox.com/s/lcgn5gvnxpo27s5/starnet_weights2.zip?dl=0">here</a>. <hr> **StarNet** is a neural network that can remove stars from images in one simple step leaving only background. More technically it is a convolutional residual net with encoder-decoder architecture and with L1, Adversarial and Perceptual losses. **Small example:** <div align="center"> <img src="https://github.com/nekitmm/starnet/blob/master/for_git/1.jpg"><br><br> </div> <center><h1>Intro</h1></center> Star removal using classical methods is a very tricky and painful multi-step procedure, which is hard to master and hard to get nice results from, especially in case of images busy with stars. This neural net will remove most of stars from input image in one step, leaving only really huge ones, and leaving (well, hopefully) intact all other small bright things whose shape is significantly different from that of a typical star, like small spiral galaxies, fine details in nebulosity, HH objects, etc. It is intended to be used by astrophotographers. Primary use is for background nebulosity enhancement in rich star fields, but it can also help in creation of nice starless image. <center><h1>Literature</h1></center> This code is partially based on pix2pix code and ideas from pix2pix paper. pix2pix code: https://github.com/phillipi/pix2pix pix2pix paper: <a href="https://arxiv.org/pdf/1611.07004v1.pdf">Image-to-Image Translation with Conditional Adversarial Networks</a> Udea of using Perceptual Adversarial losses is from this paper as well as some other ideas: <a href="https://arxiv.org/abs/1706.09138">Perceptual Adversarial Networks for Image-to-Image Transformation</a> Other papers I took ideas from or found useful during development: <a href="https://arxiv.org/abs/1701.05957">Image De-raining Using a Conditional Generative Adversarial Network</a> <a href="https://arxiv.org/abs/1606.08921">Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections</a> <a href="http://www.columbia.edu/~jwp2128/Papers/FuHuangetal2017.pdf">Removing rain from single images via a deep detail network</a> <center><h1>Usage</h1></center> Its primary purpose is to partially replace initial steps of star removal in tutorials, like one by Gerald Wechselberger, aiming to enhance nebulosity without pushing stars up. The tutorial itself was available under <a href="https://dl.dropboxusercontent.com/u/57910417/Howto_enhance_nebuala_without_pushing_stars.wmv">this</a> link, but not any more, for some reason. Haven't found any newer links to it. Anyway, you got the idea. <center><h1>Suggested Work Flow</h1></center> The transformation by this neural net can be part of PixInsight/Photoshop processing work flow. Something like this: 1. Start from **stretched** LRGB image. Save as 8 bits/channel tif file. 2. Feed to StarNet. 3. Open output in Photoshop, correct some of the worst artifacts that will most likely appear in the image. If there are huge stars in the image left, you will have to take care of them in some other way. 4. Perhaps use some noise reduction (we don't want to push noise up). 5. Use resulting image to enhance nebulosity using some method (Screen-Mask-Invert for example) or enjoy the result. 6. ? 7. Profit! <center><h1>Weights for the network</h1></center> This repository con
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:16ac758eefae90e6, topic:deep-learning, topic:neural-network, topic:tensorflow
