Post-training Quantization in TensorFlow Lite (TFLite)

Post-training quantization is a conversion technique that can reduce model size while also improving CPU and hardware accelerator latency in TensorFlow Lite (TFLite) models with little degradation in accuracy.

Link to the previous video : https://www.youtube.com/watch?v=bKLL0tAj3GE

Link to the notebook : https://github.com/bhattbhavesh91/tflite-tutorials/blob/master/tflite-part-2.ipynb

You can quantize an already-trained float TensorFlow model when you convert it to TensorFlow Lite format using the TensorFlow Lite Converter.

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Видео Post-training Quantization in TensorFlow Lite (TFLite) канала Bhavesh Bhatt