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 CNN-LSTM.keras from ab2-gbl/ECG_segmentation_using_U-Net: direct link, hf CLI and curl.
- Browser
- Download file 3.74 MB
-
https://huggingface.co/ab2-gbl/ECG_segmentation_using_U-Net/resolve/main/CNN-LSTM.keras
- Command line
-
hf download hf://ab2-gbl/ECG_segmentation_using_U-Net/CNN-LSTM.keras
-
curl -L -o CNN-LSTM.keras https://huggingface.co/ab2-gbl/ECG_segmentation_using_U-Net/resolve/main/CNN-LSTM.keras
3.74 MB
- Xet hash:
- 8954edb8800e2ade8b38343ba56691f3b0f683d8562da2e9d35dc8b2ea2c98f7
- Size of remote file:
- 3.74 MB
- SHA256:
- 81b54403a3cbc7f892a44421378ecd627e56e4d904feef809d1e7905d983f58b
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