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 saved_model.pb from MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB_full_Colombia_Dataset: direct link, hf CLI and curl.
- Browser
- Download file 5.45 MB
-
https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB_full_Colombia_Dataset/resolve/refs%2Fpr%2F2/saved_model.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB_full_Colombia_Dataset@refs/pr/2/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB_full_Colombia_Dataset/resolve/refs%2Fpr%2F2/saved_model.pb
5.45 MB
- Xet hash:
- 723e14e25b5d1381a774922840767b306e5690cab448170bee353f67af9cc296
- Size of remote file:
- 5.45 MB
- SHA256:
- e3c4007e08ad6412bcc932e571e86578d9bd1c2f834783bc051b589a36497bc3
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