Slide 1
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Child Mind Institute - Detect Sleep States
Copyright 2023 @kuto_bopro
Private LB: 0.825 (13th)
2D UNet (Efficientnet-b3)
(CV: 0.763)
2D UNet
(CV: 0.786)
CenterNet
(CV: 0.788)
1D UNet
(CV: 0.801)
1D LSTM with Wavelet
transform
(CV: 0.786)
2D UNet (EfficientNetV2-S)
(CV: 0.780)
1D UNet
(CV: 0.778)
1D UNet + WaveNet
(CV: 0.774)
Duplicate flag feature Training Techniques Post Processing Details
anglez
enmo
hour(sin,cos)
duplicate flag
diff
lead
Pipeline Overview
Tuned duration and downsample rate by each models
score_th=0.001
distance = 70
apply following post processing
(CV:0.821/public LB: 0.781)
1. 12step unit based pp
2. tolerance based pp
3. remove wakeup event at
the beginning of each
series
4. remove non pair high peak
event
5. score decay at the ending
of each series
Post processing
Find peak
Detect duplicate (artificial) wave by 15 min interval
and create flag feature weather step is duplicate
wave or not.
CV and LB were improved by about +0.005~+0.01.
1D UNet +WaveNet
(CV: 0.765)
tolerance based pp (cv+0.005)
pp to bring predicted events in tolerance 12~36 within tolerance 12.
Place the score-decayed prediction, -23 and +23 step away from the
high peak prediction(score > 0.2).
CV:0.812
Team: ricchan
Dataset
ensemble &
low pass filter
Multi task
learning
model
sleep state
(asleep/awake)
onset &wakeup event
classification
by BCEWithLogitsLoss
sleep state → binary label
event → gaussian label
regression
by L1Loss
onset → 1
wakeup → -1
other → 0
sleep state diff
step
0 1
duplicate wave
◾ other effective techniques
warmup, large negative sampling
step
high peak pred(score > 0.2)
-23 23
×1/50
×1/50
pred event score
step
12 step unit based pp (cv+0.003)
if the predicted step is a multiple of 12,
the step is shifted by -1 or +1.
original pred
shifted pred
-1 1