MJRoBot (Marcelo Rovai) / Blender - Motion Detection Public

Test data

Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.
Sample name Expected outcome Length Anomaly Accuracy Result
Low_Empty.1u3o3fu7 Low_Empty 10s 0.40 15% 62 anomaly, 21 High_Water, 15 Low_Empty, 2 uncertain, 1 Low_Water
Low_Empty.1u3o2up8 Low_Empty 10s 0.11 65% 66 Low_Empty, 26 anomaly, 8 High_Water, 1 Low_Water
Off.1u3nt2uu Off 10s -0.46 100% 101 Off
Off.1u3nsgbo Off 10s -0.46 100% 101 Off
Low_Water.1u3nb520 Low_Water 10s -0.31 54% 55 Low_Water, 35 High_Water, 7 Low_Empty, 4 uncertain
High_Water.1u3n9h56 Low_Water 10s -0.30 65% 66 Low_Water, 32 High_Water, 2 uncertain, 1 Low_Empty
High_Water.1u3n1t8b High_Water 10s -0.26 45% 45 High_Water, 24 Low_Empty, 22 uncertain, 10 Low_Water
High_Water.1u3n1a1t High_Water 10s -0.29 21% 71 Low_Water, 21 High_Water, 7 uncertain, 1 Low_Empty, 1 Off
Low_Empty.1u3errfg Low_Empty 10s -0.42 89% 90 Low_Empty, 7 High_Water, 2 Off, 2 uncertain
High_Water.1u3f2kqe High_Water 10s -0.24 100% 101 High_Water
Low_Water.1u3evdk3 Low_Water 10s -0.34 88% 89 Low_Water, 6 High_Water, 4 uncertain, 2 Off
Off.1u3edqmo Off 10s 0.06 37% 37 Off, 27 anomaly, 23 Low_Water, 10 Low_Empty, 4 High_Water

Model testing output

Model testing results

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