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Up to this point the model seemed to perform okay, although the "circle" gesture was a bit tricky to trigger.
After commit cbddbc2 the "Data Processor" samples 80 accelerometer data points, as done in the ML Trainer. And from that point when the micro:bit is not moving in the "face up" position, the "circle" ML gesture is constantly triggered.
Changing the orientation of the micro:bit does seem to correctly trigger the "still" gesture when not moving, and shaking does works well.
Taking the parent commit e0e8e5e, and changing the mlDataProcessor.init(inputLen / 3) call to mlDataProcessor.init(80), produces the same result, so that would discard a different issue or bug introduced by cbddbc2.
We'll need to check if this is an issue converting the model with ML4F, or if there is a bug in the implementation of the data processing before feeding it into the model.
Things we need to double check:
The ML trainner sampling period is 25ms
The ML trainer processes 80 samples for each model inferance
Does the changing the Data processor filter
The text was updated successfully, but these errors were encountered:
microbit-carlos
changed the title
ML4F Model seems to perform worse when the right number of samples were implemented
ML4F Model seems to perform worse when the using the correct number of samples
Jun 4, 2024
Before commit cbddbc2 the "Data Processor" was only collecting 8 samples with the built-in model (model input length of 24 divided by 3):
https://github.com/microbit-foundation/pxt-ml-runner-poc/blob/e0e8e5e1494efe58b0c783a1297c6a1b7d6291aa/pxtextension.cpp#L109
Up to this point the model seemed to perform okay, although the "circle" gesture was a bit tricky to trigger.
After commit cbddbc2 the "Data Processor" samples 80 accelerometer data points, as done in the ML Trainer. And from that point when the micro:bit is not moving in the "face up" position, the "circle" ML gesture is constantly triggered.
Changing the orientation of the micro:bit does seem to correctly trigger the "still" gesture when not moving, and shaking does works well.
Taking the parent commit e0e8e5e, and changing the
mlDataProcessor.init(inputLen / 3)
call tomlDataProcessor.init(80)
, produces the same result, so that would discard a different issue or bug introduced by cbddbc2.We'll need to check if this is an issue converting the model with ML4F, or if there is a bug in the implementation of the data processing before feeding it into the model.
Things we need to double check:
The text was updated successfully, but these errors were encountered: