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Non-determinism in image classification #40
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Here is an example of some of the predictions from the frame classifier. Run 1:
Run 2:
In this case the differences are not large and in both runs we end up with a TimeFrame from 22000 through 26000 albeit with different scores. |
I think this is expected as neither trainer nor classifier is implemented in the torch "deterministic mode". We can experiment with adding the mode setter in the classifier code and see if that works...? |
When I run # process_swt30.py "summarizes" MMIF into start, end, and frame type of each TimeFrame annotation into a line
$ for mmif in c53*.mmif; do py process_swt30.py $mmif | wc -l ; done
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# getting sort_uniq
$ for mmif in c53*.mmif; do py process_swt30.py $mmif | wc -l ; done | sort -u
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22 , the numbers of detected "relevant" time frames are quite different ranging 14-22. |
new report from @owencking , results of running SWT 7.1 twice on the same file with the same configs: https://drive.google.com/drive/folders/1e4W23b5MA6gw01oRBDC2oyGyvLmmEa51 |
Because
In the tip of the refactor-feature-extraction branch you get different image classification results if you run the classifier from the app repeatedly. The problem is probably in one of two lines:
app-swt-detection/classify.py
Lines 77 to 78 in 6ab33ba
This needs some more poking.
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