Instructions to use MITCriticalData/Sentinel-2_ViT_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_ViT_Autoencoder_RGB_full_Colombia_Dataset with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset: direct link, hf CLI and curl.
- Browser
- Download file 15.9 MB
-
https://huggingface.co/MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset/resolve/refs%2Fpr%2F1/saved_model.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset@refs/pr/1/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB_full_Colombia_Dataset/resolve/refs%2Fpr%2F1/saved_model.pb
15.9 MB
- Xet hash:
- 571b49e3095a57f3c085c8bdec579d471dcb1b97bbe2cf0df618645d81188d2d
- Size of remote file:
- 15.9 MB
- SHA256:
- c1a431f237d17c25818be2a62df5c1fc1d424f25f4c247397a4601fb678ca70c
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