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How do I optimize and deploy a deep learning model on an ESP32?

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How do i optimize and deploy a deep learning model on an ESP32? still based on my project image recognition system that can analyze images of tissue samples, identify malignancies, and predict possible symptoms and causes. Am currently trying to deploy the trained model on the ESP32 for real-time inference.
But

    MemoryError: Model size exceeds available memory

How do I go about resolving this 

    import tensorflow as tf
    from tensorflow.keras.models import load_model
    import tensorflow.lite as tflite

    
    model = load_model('malignant_tissue_model.h5')

   
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    tflite_model = converter.convert()

   
    with open('malignant_tissue_model.tflite', 'wb') as f:
        f.write(tflite_model)
  1. Enthernet Code#0000
    November 21, 2024 at 10:28 pm

    @bosslady0299 To deploy your deep learning model for image recognition on the `ESP32`, you need to optimize it to address memory constraints. The `MemoryError` occurs because the model is too large for the `ESP32’s` available memory. To resolve this, you can:

    – Quantize the Model: Convert the model to an `8-bit` format using `TensorFlow Lite’s` post-training `quantization`, which significantly reduces the `model` size and `memory` usage.

    – Simplify the Model: Reduce the complexity by using fewer layers, neurons, or switching to more efficient architectures like `MobileNet` or `TinyML` models.

    – Use Additional Optimizations: Techniques like pruning or weight clustering can further shrink the model.

    Once optimized, test the model on the `ESP32` to ensure it fits and runs inference efficiently.

  2. Boss lady#0000
    November 21, 2024 at 10:28 pm

    Thanks for the help would work on this now

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Modbus Protocol Implementation on STM32

High Voltage Section: MOSFET Burn Issue Due to Dead Time

Testing High Frequency Inverter Board with Short Circuit Protection