Instructions to use Recompense/FoodVision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Recompense/FoodVision 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://Recompense/FoodVision") - Notebooks
- Google Colab
- Kaggle
Download model.keras from Recompense/FoodVision: direct link, hf CLI and curl.
- Browser
- Download file 32.1 MB
-
https://huggingface.co/Recompense/FoodVision/resolve/main/model.keras
- Command line
-
hf download hf://Recompense/FoodVision/model.keras
-
curl -L -o model.keras https://huggingface.co/Recompense/FoodVision/resolve/main/model.keras
32.1 MB
- Xet hash:
- 9a6921a9f10af546fd67d8395b665fe65e5b4415076136bed8556d7de1a41847
- Size of remote file:
- 32.1 MB
- SHA256:
- 5fc07f257dff8d5195127e0921a4068cd570daf231e63d961b86af8ce2431cd8
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