Model Card for DiffuSETS

Description

  • Developed by: Yongfan Lai
  • License: mit

Source Code

Assets List

  • prerequisites_for_inference.zip:
prerequisites/
β”œβ”€β”€ clf_data/ : Directory contains data for imbalanced classification experiments 
β”œβ”€β”€ clip_model.pth : CLIP model checkpoints.
β”œβ”€β”€ mimic_vae_lite_0_new.pt : Subsets of VAE encoding latents of MIMIC IV ECG dataset.
β”œβ”€β”€ ptbxl_text_embed.csv : Pre-generated text embeddings of PTB-XL dataset
β”œβ”€β”€ ptbxl_vae.pt : VAE encoding latents of PTB-XL dataset.
β”œβ”€β”€ unet_all.pth : A pretrained full capacity DiffuSETS model.
└── vae_model.pth : VAE model checkpoints suit for the unet model and latent datasets above.
  • mimic_vae_lite_0_new.zip:
prerequisites/
└── mimic_vae_0_new.pt: VAE encoding latents of MIMIC IV ECG dataset. Only needed while training.

Usage

Quick Start: Download and unzip the prerequisites_for_inference.zip, place the ./prerequisites folder under repo's root, then start ECG generation through

python DiffuSETS_inference.py path/to/your/config.json

or

python -m test_scripts.diversity

For users want to train from scratch: Besides above prerequisites_for_inference.zip, the mimic_vae_0_new.pt (training data encoded by vae) should also be downloaded and put into the ./prerequisites folder.

For more details about training and inference, please refer to our github repo page

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