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:
# 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_Resnet50V2_VariationalAutoencoder_RGB") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bff1a3bf02847269c60d31e35c3c97e6980769d3066dbfd1c0fa2700a36fbeda
- Size of remote file:
- 836 kB
- SHA256:
- e4aaaf5d892ca0522620e65f56541d3e4dce5fe5af4f2b022fc9136fbb9f8cd8
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