Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB_full_Colombia_Dataset 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_full_Colombia_Dataset 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_full_Colombia_Dataset") - Notebooks
- Google Colab
- Kaggle
Download variables/variables.index from MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB_full_Colombia_Dataset: direct link, hf CLI and curl.
- Browser
- Download file 17.2 kB
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https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB_full_Colombia_Dataset/resolve/main/variables/variables.index
- Command line
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hf download hf://MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB_full_Colombia_Dataset/variables/variables.index
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curl -L -o variables.index https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB_full_Colombia_Dataset/resolve/main/variables/variables.index
17.2 kB
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