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Quantization (post-training quantization) your (custom mobilenet_v2) models .h5 or .pb models using TensorFlow Lite 2.4

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DEFAULT_float16.py
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DEFAULT_float8.py
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OPTIMIZE_FOR_LATENCY_float16.py
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OPTIMIZE_FOR_SIZE_float16.py
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OPTIMIZE_FOR_SIZE_float8.py
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README.md
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mobilenetv2.h5
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quantization_DEFAULT_8bit_model_h5_to_tflite.tflite
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quantization_OPTIMIZE_FOR_LATENCY_8bit_model_h5_to_tflite.tflite
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quantization_OPTIMIZE_FOR_LATENCY_float16_model_h5_to_tflite.tflite
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simple_tflite.tflite
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test_h5.py
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test_tflite.py
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Quantization (post-training quantization) your (custom mobilenet_v2) models .h5 or .pb models using TensorFlow Lite 2.4

REAME and all comments FOR THIS REPOSTITORY HERE (this article):

https://oleksandrg.medium.com/quantization-your-custom-mobilenet-v2-models-h5-models-using-tensorflow-lite-2-4-5e7046fa860e

Also

If you were interested in this code, please put a star ✰ for this repo

Also, if you were interested in my article, please subscribe to my blog

https://oleksandrg.medium.com/

Thank you 🙂

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Quantization (post-training quantization) your (custom mobilenet_v2) models .h5 or .pb models using TensorFlow Lite 2.4

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tensorflow-tutorials quantization tensorflow-examples tensorflowlite quantization-algorithms tensorflow-quantum-classification tensorflow-quantum-example

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