/RhythmNet

End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation. A replication of the RhythmNet model.

Primary LanguagePythonMIT LicenseMIT

RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation

A reproduction of the RhythmNet model. Paper link

Dataset:

VIPL-HR dataset

Experiments

Shared parameters:

batch size: 32
Dataset: VIPL
Model: RhythmNet
initial learning rate: 1e-3
epochs: 50
window_size = 300 frames with stride of 0.5 seconds

Dataset-split: 5 fold validation

Experiment for 1-Fold without GRU layer

Set Loss MAE (bpm) RMSE (bpm)
Training 3.096 1.817 2.834
Eval 15.91 9.255 11.787

Experiment for 1-Fold with GRU layer

Set Loss MAE (bpm) RMSE (bpm)
Training 3.925 2.423 4.16
Eval 14.25 13.992 17.019