Instructions to use MITCriticalData/Sentinel-2_Resnet50V2_Autoencoder_RGB 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 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_Autoencoder_RGB") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff06cab00f5ee4a0b482be9b7ca838e8fb1433bbcecbde241f8fa8c97797f54a
- Size of remote file:
- 5.44 MB
- SHA256:
- 2175da3ac1066a236cdd9f23c137205be89282cd81fb2e608558cb69667be6f9
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