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 mlp_normal_vs_abnormal_model.pkl from ab2-gbl/ECG_segmentation_using_U-Net: direct link, hf CLI and curl.
- Browser
- Download file 671 kB
-
https://huggingface.co/ab2-gbl/ECG_segmentation_using_U-Net/resolve/main/mlp_normal_vs_abnormal_model.pkl
- Command line
-
hf download hf://ab2-gbl/ECG_segmentation_using_U-Net/mlp_normal_vs_abnormal_model.pkl
-
curl -L -o mlp_normal_vs_abnormal_model.pkl https://huggingface.co/ab2-gbl/ECG_segmentation_using_U-Net/resolve/main/mlp_normal_vs_abnormal_model.pkl
671 kB
- Xet hash:
- 9950f2b92ca21ce9299bf80fa1e4a6e8bbb3ac67e3142c23ceebae8c700fac89
- Size of remote file:
- 671 kB
- SHA256:
- 1c0ae35549ae643aa7091dce6a20b2189840352d187ff3add4b0e50c5d3f53f2
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