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Considered leaving this on a branch as the results were so inconclusive, but there's some useful utils and improved test coverage in here as well that seem worth a merge - would appreciate eyes on it.
What's in here
Tiny bit of refactoring for ease of loading the scivision model either with or without its final layer (the classification one)
Associated tests, increased overall coverage
A notebook which leans heavily on the Captum tutorial walkthroughs, applying a range of methods to heatmap areas of plankton images which have influenced the predicted class
The visualisations don't look very meaningful to me, wrote a bit about probable reasons why, and what's worth doing next, in #6
To test
run the python tests as per the README (minimum: export PYTHONPATH=.; py.test
I've switched this back out of draft status and added a few final touches with non-plankton images and a generic ImageNet set of weights. The outcome's still really open, but there are higher priority things to work on - the fact that I can't easily connect this line of work to anything active on the RSE group discussions forum is a helpful indicator of that!
Abandoning this, the outcome wasn't very illuminating, can easily be reconstructed, and the underlying changes (dropping scivision, shifting the project layout) would make reconstruction less effort than merging
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Considered leaving this on a branch as the results were so inconclusive, but there's some useful utils and improved test coverage in here as well that seem worth a merge - would appreciate eyes on it.
What's in here
scivision
model either with or without its final layer (the classification one)The visualisations don't look very meaningful to me, wrote a bit about probable reasons why, and what's worth doing next, in #6
To test
export PYTHONPATH=.; py.test
Run all
- it needs the location of the s3 object store set in.env
- see also the README