Instructions to use Aditya2162/ivus-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Aditya2162/ivus-segmentation with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Aditya2162/ivus-segmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd2572f88999747f4052b03d48c45b2ed3da595beb09ff11544a2467676a3448
- Size of remote file:
- 28.3 MB
- SHA256:
- 7494a4ae42c498ac7ade0bfc021042fbedbaf54c46f5885c32e24a4478fb0c31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.