Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_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_VariationalAutoencoder_RGB") - Notebooks
- Google Colab
- Kaggle
Download keras_metadata.pb from MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB: direct link, hf CLI and curl.
- Browser
- Download file 836 kB
-
https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB/resolve/main/keras_metadata.pb
- Command line
-
hf download hf://MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB/keras_metadata.pb
-
curl -L -o keras_metadata.pb https://huggingface.co/MITCriticalData/Sentinel-2_Resnet50V2_VariationalAutoencoder_RGB/resolve/main/keras_metadata.pb
836 kB
- Xet hash:
- bff1a3bf02847269c60d31e35c3c97e6980769d3066dbfd1c0fa2700a36fbeda
- Size of remote file:
- 836 kB
- SHA256:
- e4aaaf5d892ca0522620e65f56541d3e4dce5fe5af4f2b022fc9136fbb9f8cd8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.