Instructions to use Steenslid/ecg-ptbxl-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Steenslid/ecg-ptbxl-classification 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://Steenslid/ecg-ptbxl-classification") - Notebooks
- Google Colab
- Kaggle
Download normalisation_params.npz from Steenslid/ecg-ptbxl-classification: direct link, hf CLI and curl.
- Browser
- Download file 596 Bytes
-
https://huggingface.co/Steenslid/ecg-ptbxl-classification/resolve/main/normalisation_params.npz
- Command line
-
hf download hf://Steenslid/ecg-ptbxl-classification/normalisation_params.npz
-
curl -L -o normalisation_params.npz https://huggingface.co/Steenslid/ecg-ptbxl-classification/resolve/main/normalisation_params.npz
596 Bytes
- Xet hash:
- 6ca5a447aaffe91202ce7eb46a2034206b229e9fef8540565c270ab736d77b5d
- Size of remote file:
- 596 Bytes
- SHA256:
- 9a496322e40c88f8a8657ff18e04dbe2bc1455eba6d041cfe823f5f9d31cfce3
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