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 saved_model.pb from MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB: direct link, hf CLI and curl.
- Browser
- Download file 5.44 MB
-
https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB/resolve/main/saved_model.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB/resolve/main/saved_model.pb
5.44 MB
- Xet hash:
- ff06cab00f5ee4a0b482be9b7ca838e8fb1433bbcecbde241f8fa8c97797f54a
- Size of remote file:
- 5.44 MB
- SHA256:
- 2175da3ac1066a236cdd9f23c137205be89282cd81fb2e608558cb69667be6f9
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