3 replies

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

    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)

    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

ESP32

, you need to optimize it to address memory constraints. The

MemoryError

MemoryError

occurs because the model is too large for the

ESP32’s

ESP32’s

available memory. To resolve this, you can:

- Quantize the Model: Convert the model to an

8-bit

8-bit

format using

TensorFlow Lite’s

TensorFlow Lite’s

post-training

quantization

quantization

, which significantly reduces the

model

model

size and

memory

memory

usage.

- Simplify the Model: Reduce the complexity by using fewer layers, neurons, or switching to more efficient architectures like

MobileNet

MobileNet

TinyML

TinyML

models.

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

Once optimized, test the model on the

ESP32

ESP32

to ensure it fits and runs inference efficiently.

Jump to solution

DIIS

DevHeads IoT Integration Server•2y ago•

3 replies

Boss lady

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

    MemoryError: Model size exceeds available memory

    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)

    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

ESP32

, you need to optimize it to address memory constraints. The

MemoryError

MemoryError

occurs because the model is too large for the

ESP32’s

ESP32’s

available memory. To resolve this, you can:

- Quantize the Model: Convert the model to an

8-bit

8-bit

format using

TensorFlow Lite’s

TensorFlow Lite’s

post-training

quantization

quantization

, which significantly reduces the

model

model

size and

memory

memory

usage.

- Simplify the Model: Reduce the complexity by using fewer layers, neurons, or switching to more efficient architectures like

MobileNet

MobileNet

TinyML

TinyML

models.

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

Once optimized, test the model on the

ESP32

ESP32

to ensure it fits and runs inference efficiently.

Jump to solution

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?

Similar Threads

Similar Threads

Similar Threads