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:
- 5ac4d1cfb4a3ad641122a4851c82e962683c07db5b8da0251bf51b53d1c04a18
- Size of remote file:
- 5.44 MB
- SHA256:
- c66a93ca5a80cd328d716b43774a51db71e952d6774f2fec4cfc8b03b9a85d1c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.