DevHeads IoT Integration ServerDIIS
DevHeads IoT Integration Server•15mo ago
Boss lady

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

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)
Solution
@Boss lady 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.
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