consider
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README.md
44
README.md
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@ -106,7 +106,7 @@ The following steps need to be performed:
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1. Organize downloaded waterfalls into categories (e.g. "good", "bad", "failed").
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Use `wut-dl-sort` script.
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The script them into their respective directories under:
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The script will sort them into their respective directories under:
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* `data/train/good/`
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* `data/train/bad/`
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* `data/train/failed/`
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@ -143,6 +143,48 @@ mkdir download
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rsync -ultav rsync://ml.spacecruft.org/download/ download/
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```
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# TODO / Brainstorms
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This is a first draft of how to do this. The actual machine learning
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process hasn't been looked at at all, except to get it to generate
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an answer. It has a long ways to go. There are also many ways to do
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this besides using Tensorflow and Keras. Originally, I considered
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using OpenCV. Ideas in no particular order below.
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## General
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General considerations.
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* Use Open CV.
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* Use something other than Tensorflow / Keras.
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* Do mirror of `network.satnogs.org` and do API calls to it for data.
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* Mirror data/ dir in Apache/rsync too.
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## Tensorflow / Keras
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At present Tensorflow and Keras are used.
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* Learn Keras / Tensorflow...
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* What part of image is being evaluated?
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* Re-evaluate each step.
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* Right now the prediction output is just "good" or "bad", needs
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"failed" too.
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* Give confidence score in each prediction.
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* Visualize what ML is looking at.
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* Separate out good/bad/failed by satellite, transmitter, or encoding.
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This way "good" isn't considering a "good" vetting to be a totally
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different encoding. Right now, it is considering as good observations
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that should be bad...
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* If it has a low confidence, return "unknown" instead of "good" or "bad".
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# Caveats
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This is the first machine learning script I've done,
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I know little about satellites and less about radio,
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