1
converter = tf.lite.TFLiteConverter.from_keras_model(model)
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tflite_model = converter.convert()
3

4
with open('mobilenet_v2_1.0_224.tflite', 'wb') as f:
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  f.write(tflite_model)

1
# A generator that provides a representative dataset
2
def representative_data_gen():
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  dataset_list = tf.data.Dataset.list_files('/content/fruits-360_dataset/fruits-360/*/*.jpg')
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  #Input the dataset files
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  for i in range(100):
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    image = next(iter(dataset_list))
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    image = tf.io.read_file(image)
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    image = tf.io.decode_jpeg(image, channels=3)
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    image = tf.image.resize(image, [IMAGE_SIZE, IMAGE_SIZE])
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    image = tf.cast(image / 255., tf.float32)
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    image = tf.expand_dims(image, 0)
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    yield [image]
13

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converter = tf.lite.TFLiteConverter.from_keras_model(model)
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# This enables quantization
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converter.optimizations = [tf.lite.Optimize.DEFAULT]
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# This sets the representative dataset for quantization
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converter.representative_dataset = representative_data_gen
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# This ensures that if any ops can't be quantized, the converter throws an error
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converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
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# For full integer quantization, though supported types defaults to int8 only, we explicitly declare it for clarity.
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converter.target_spec.supported_types = [tf.int8]
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# These set the input and output tensors to uint8 (added in r2.3)
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converter.inference_input_type = tf.uint8
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converter.inference_output_type = tf.uint8
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tflite_model = converter.convert()
27

28
with open('mobilenet_v2_1.0_224_quant.tflite', 'wb') as f:
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  f.write(tflite_model)

1
! curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
2

3
! echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list
4

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! sudo apt-get update
6

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! sudo apt-get install edgetpu-compiler
8

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! edgetpu_compiler mobilenet_v2_1.0_224_quant.tflite

1
python3 classify_image.py \
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  --model mobilenet_v2_1.0_224_quant_edgetpu.tflite \
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  --labels fruit_labels.txt \
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  --input fruit.jpg