Instructions to use WheelsTransit/HK-TransitFlow-Net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use WheelsTransit/HK-TransitFlow-Net with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://WheelsTransit/HK-TransitFlow-Net") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -24,7 +24,7 @@ A Deep Neural Network for predicting bus travel times in Hong Kong.
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The published version is mapped to used with [hk-bus-crawling](https://github.com/hkbus/hk-bus-crawling)
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This is an open weight model,
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This is not Wheels Atlas, nor it is trained using the same way Atlas is.
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The published version is mapped to used with [hk-bus-crawling](https://github.com/hkbus/hk-bus-crawling)
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This is an open weight model, trained using data collected by Wheels.
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This is not Wheels Atlas, nor it is trained using the same way Atlas is.
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