Instructions to use MITCriticalData/Sentinel-2_ViT_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_ViT_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_ViT_Autoencoder_RGB_full_Colombia_Dataset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eab9a1c1f030e43726b66785d250d53f8f33c9ead7a14ecee1348435cd553e83
- Size of remote file:
- 214 kB
- SHA256:
- 72e9c2389aa34c4d33fc6ab68618da767284d3cbb3883ab46683ced706ad5137
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.