Instructions to use ab2-gbl/ECG_segmentation_using_U-Net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ab2-gbl/ECG_segmentation_using_U-Net with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://ab2-gbl/ECG_segmentation_using_U-Net") - Notebooks
- Google Colab
- Kaggle
Download ecg_multi_class_model.pkl from ab2-gbl/ECG_segmentation_using_U-Net: direct link, hf CLI and curl.
- Browser
- Download file 103 MB
-
https://huggingface.co/ab2-gbl/ECG_segmentation_using_U-Net/resolve/main/ecg_multi_class_model.pkl
- Command line
-
hf download hf://ab2-gbl/ECG_segmentation_using_U-Net/ecg_multi_class_model.pkl
-
curl -L -o ecg_multi_class_model.pkl https://huggingface.co/ab2-gbl/ECG_segmentation_using_U-Net/resolve/main/ecg_multi_class_model.pkl
103 MB
- Xet hash:
- e1c205ec7e57961ae28b41c7c7a74f83e5f4f2b158f13f2d5296301a04e6c22e
- Size of remote file:
- 103 MB
- SHA256:
- d7fc8f19a1abec8cf421bf18b786a3f548842bf26a56f584199141518a0f0e78
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