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 R_detection.h5 from ab2-gbl/ECG_segmentation_using_U-Net: direct link, hf CLI and curl.
- Browser
- Download file 129 kB
-
https://huggingface.co/ab2-gbl/ECG_segmentation_using_U-Net/resolve/main/R_detection.h5
- Command line
-
hf download hf://ab2-gbl/ECG_segmentation_using_U-Net/R_detection.h5
-
curl -L -o R_detection.h5 https://huggingface.co/ab2-gbl/ECG_segmentation_using_U-Net/resolve/main/R_detection.h5
129 kB
- Xet hash:
- 4d05a7fa4a51973936946dd29f6ae1b7ba37703a8fe63892350ceea659e288ad
- Size of remote file:
- 129 kB
- SHA256:
- 7df9bb2457f64d360ecf7f48d02794fdcc94ecba1775c9aba309f793d696f6f3
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