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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Steenslid/ecg-ptbxl-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ca5a447aaffe91202ce7eb46a2034206b229e9fef8540565c270ab736d77b5d
- Size of remote file:
- 596 Bytes
- SHA256:
- 9a496322e40c88f8a8657ff18e04dbe2bc1455eba6d041cfe823f5f9d31cfce3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.