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:
- a8faffe970ea12f16ee43a821ba0eaf5c49cc8322071220ad179aa9d30040a15
- Size of remote file:
- 30.3 MB
- SHA256:
- ff3a63757f3574f8aa138e6ddf0b69c3d0bd6b7acade47636b3d49ee224f9584
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.