Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB 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://MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB") - Notebooks
- Google Colab
- Kaggle
Download keras_metadata.pb from MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB: direct link, hf CLI and curl.
- Browser
- Download file 828 kB
-
https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB/resolve/main/keras_metadata.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB/keras_metadata.pb
-
curl -L -o keras_metadata.pb https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB/resolve/main/keras_metadata.pb
828 kB
- Xet hash:
- c8f45a4a9c6f73b0204878b3a71a4e10857ba5152d9ebb45c4782b00628889d6
- Size of remote file:
- 828 kB
- SHA256:
- 487e776e14c93f476d33eac09eb0fadf1b977313ba9bd29e8400b8f8de91de94
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