Instructions to use KxSystems/mri_resnet_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use KxSystems/mri_resnet_model 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://KxSystems/mri_resnet_model") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from KxSystems/mri_resnet_model: direct link, hf CLI and curl.
- Browser
- Download file 3.95 MB
-
https://huggingface.co/KxSystems/mri_resnet_model/resolve/main/saved_model.pb
- Command line
-
hf download hf://KxSystems/mri_resnet_model/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/KxSystems/mri_resnet_model/resolve/main/saved_model.pb
3.95 MB
- Xet hash:
- 4105f8084daa596f60dfb818110d05d07389ff8c632d3c67d23a9dad1ecd0133
- Size of remote file:
- 3.95 MB
- SHA256:
- 76c9e0902c3da0f76e26737463d825ffa9b8e45149495cfcd300b723f77eccc7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.