Instructions to use rishab1090/potato with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use rishab1090/potato with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://rishab1090/potato") - Notebooks
- Google Colab
- Kaggle
| # Auto detect text files and perform LF normalization | |
| * text=auto | |
| unet_model.h5 filter=lfs diff=lfs merge=lfs -text | |
| unet_model.keras filter=lfs diff=lfs merge=lfs -text | |
| unet_tf.keras filter=lfs diff=lfs merge=lfs -text | |