Skip to content

Latest commit

 

History

History
executable file
·
67 lines (34 loc) · 2 KB

README.md

File metadata and controls

executable file
·
67 lines (34 loc) · 2 KB

PhysMamba

PhysMamba_frame

SSSD

🔧 Setup

STEP1: bash setup1.sh

STEP2: conda activate rppg-toolbox

STEP3: bash setup2.sh

The codes are based on rPPG-toolbox

💻 Examples of Neural Network Training

Please use config files under ./configs/train_configs

Intra-dataset on MMPD With PhysMamba

STEP 1: Download the MMPD raw data by asking the paper authors

STEP 2: Modify ./configs/train_configs/intra/0MMPD_PhysMamba.yaml

STEP 3: Run python main.py --config_file ./configs/train_configs/intra/0MMPD_PhysMamba.yaml

Intra-dataset on UBFC With PhysMamba

STEP 1: Download the UBFC raw data by asking the paper authors

STEP 2: Modify ./configs/train_configs/intra/0UBFC_PhysMamba.yaml

STEP 3: Run python main.py --config_file ./configs/train_configs/intra/0UBFC_PhysMamba.yaml

Intra-dataset on PURE With PhysMamba

STEP 1: Download the PURE raw data by asking the paper authors

STEP 2: Modify ./configs/train_configs/intra/0PURE_PhysMamba.yaml

STEP 3: Run python main.py --config_file ./configs/train_configs/intra/0PURE_PhysMamba.yaml

Heart Rate Visual

Cross-dataset - Training on MMPD and testing on PURE With PhysMamba

STEP 1: Download the PURE raw data by asking the paper authors

STEP 2: Download the MMPD raw data by asking the paper authors

STEP 3: Modify ./configs/train_configs/cross/MMPD_PURE_PhysMamba.yaml

STEP 4: Run python main.py --config_file ./configs/train_configs/cross/MMPD_PURE_PhysMamba.yaml

Cross-dataset - Training on PURE and testing on MMPD With PhysMamba

STEP 1: Download the PURE raw data by asking the paper authors

STEP 2: Download the MMPD raw data by asking the paper authors

STEP 3: Modify ./configs/train_configs/cross/PURE_MMPD_PhysMamba.yaml

STEP 4: Run python main.py --config_file ./configs/train_configs/cross/PURE_MMPD_PhysMamba.yaml