Instructions to use otainfo/esp32-process-success-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use otainfo/esp32-process-success-predictor 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://otainfo/esp32-process-success-predictor") - Notebooks
- Google Colab
- Kaggle
Commit History
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typo fixed
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Added link guide which explains how to use this model for any CPU/MCU that support TFLite
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Help user onboard using sample code
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ESP32 process predictor models version 1
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initial commit
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