Instructions to use TweeeZT/Nutrivision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use TweeeZT/Nutrivision 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://TweeeZT/Nutrivision") - Notebooks
- Google Colab
- Kaggle
Download final_model.h5 from TweeeZT/Nutrivision: direct link, hf CLI and curl.
- Browser
- Download file 39.2 MB
-
https://huggingface.co/TweeeZT/Nutrivision/resolve/main/final_model.h5
- Command line
-
hf download hf://TweeeZT/Nutrivision/final_model.h5
-
curl -L -o final_model.h5 https://huggingface.co/TweeeZT/Nutrivision/resolve/main/final_model.h5
39.2 MB
- Xet hash:
- a961c712bc705771b7bedd917a1aa711b4ee59f1f9b52d6cb59db39fa3ccec54
- Size of remote file:
- 39.2 MB
- SHA256:
- 2bff68310866a0a1f7aca6629ff573ab188cf47a8e16eadc502c0d3ff5132bfc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.