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:
# 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_full_Colombia_Dataset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8f45a4a9c6f73b0204878b3a71a4e10857ba5152d9ebb45c4782b00628889d6
- Size of remote file:
- 828 kB
- SHA256:
- 487e776e14c93f476d33eac09eb0fadf1b977313ba9bd29e8400b8f8de91de94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.