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
feat(models): upload SpatialGCN_best.keras
Browse filesAuthored by Danesh Selwal <selwaldanesh@gmail.com>
Co-authored-by: Claude <claude@anthropic.com>
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multicp/models/ViT_UNet_MultiHead_best.keras filter=lfs diff=lfs merge=lfs -text
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multicp/models/ViT_UNet_MultiHead_final.keras filter=lfs diff=lfs merge=lfs -text
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spatialgcn/models/SpatialGCN_final.keras filter=lfs diff=lfs merge=lfs -text
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multicp/models/ViT_UNet_MultiHead_best.keras filter=lfs diff=lfs merge=lfs -text
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multicp/models/ViT_UNet_MultiHead_final.keras filter=lfs diff=lfs merge=lfs -text
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spatialgcn/models/SpatialGCN_final.keras filter=lfs diff=lfs merge=lfs -text
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spatialgcn/models/SpatialGCN_best.keras filter=lfs diff=lfs merge=lfs -text
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spatialgcn/models/SpatialGCN_best.keras
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