You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

Learning ECG Image Representations via Dual Physiological-Aware Alignments

Quickstart

from transformers import AutoModel, CLIPImageProcessor
from PIL import Image
import torch

model = AutoModel.from_pretrained("Manhph2211/ECG-Scan", trust_remote_code=True)
model.eval()

processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14-336")
img = Image.open("ecg.png").convert("RGB")
pixel_values = processor(images=img, return_tensors="pt")["pixel_values"]

with torch.no_grad():
    out = model(pixel_values).embeddings         

Citation

@article{pham2026learning,
  title={Learning ECG Image Representations via Dual Physiological-Aware Alignments},
  author={Pham, Hung Manh and Tang, Jialu and Saeed, Aaqib and Ma, Dong and Zhu, Bin and Zhou, Pan},
  journal={arXiv preprint arXiv:2604.01526},
  year={2026}
}
Downloads last month
124
Safetensors
Model size
0.3B params
Tensor type
F32
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Paper for Manhph2211/ECG-Scan