Keras
English
remote-sensing
hyperspectral
uncertainty-quantification
conformal-prediction
mamba
foundation-models
Instructions to use DaneshSelwal/Classification-DIAS-65Bands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DaneshSelwal/Classification-DIAS-65Bands with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://DaneshSelwal/Classification-DIAS-65Bands") - Notebooks
- Google Colab
- Kaggle
Download spatialgcn/models/SpatialGCN_final.keras from DaneshSelwal/Classification-DIAS-65Bands: direct link, hf CLI and curl.
- Browser
- Download file 718 kB
-
https://huggingface.co/DaneshSelwal/Classification-DIAS-65Bands/resolve/main/spatialgcn/models/SpatialGCN_final.keras
- Command line
-
hf download hf://DaneshSelwal/Classification-DIAS-65Bands/spatialgcn/models/SpatialGCN_final.keras
-
curl -L -o SpatialGCN_final.keras https://huggingface.co/DaneshSelwal/Classification-DIAS-65Bands/resolve/main/spatialgcn/models/SpatialGCN_final.keras
718 kB
- Xet hash:
- 42de714033c32c42ed102b8da0158b1701b75d6d1e56441d25c2f4ef8d5d3c04
- Size of remote file:
- 718 kB
- SHA256:
- db27c60c7e308e6c576175de1dbee0fbd22065d82cf2c831f376b1f33656de23
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