Instructions to use MIDSCapstoneTeam/ContrailSentinel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MIDSCapstoneTeam/ContrailSentinel 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://MIDSCapstoneTeam/ContrailSentinel") - Notebooks
- Google Colab
- Kaggle
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README.md
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Use the code below to get started with the model.
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notebook_login()
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from huggingface_hub import from_pretrained_keras
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model = from_pretrained_keras("MIDSCapstoneTeam/ContrailSentinel", custom_objects={'dice_loss_plus_5focal_loss': total_loss, 'jaccard_coef': jaccard_coef, 'IOU score' : sm.metrics.IOUScore(threshold=0.9, name="IOU score"), 'Dice Coeficient' : sm.metrics.FScore(threshold=0.6, name="Dice Coeficient")}, compile=False)
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## Training and evaluation data
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Use the code below to get started with the model.
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```
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from huggingface_hub import notebook_login
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notebook_login()
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from huggingface_hub import from_pretrained_keras
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model = from_pretrained_keras("MIDSCapstoneTeam/ContrailSentinel", custom_objects={'dice_loss_plus_5focal_loss': total_loss, 'jaccard_coef': jaccard_coef, 'IOU score' : sm.metrics.IOUScore(threshold=0.9, name="IOU score"), 'Dice Coeficient' : sm.metrics.FScore(threshold=0.6, name="Dice Coeficient")}, compile=False)
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```
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## Training and evaluation data
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