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The application uses OpenCV 4.0.1 to pre-process the input image and TensorFlow Lite (TFL) 1.12 as inference engine. The model, originally created and trained with Keras of TensorFlow (TF) 1.15, was therefore converted into the TFL format.
Then, the same model was recreated and retrained with Keras of TF 1.12. This allowed to convert it into TFL with post-training quantization of the weights without compatibility issues with the target inference engineversion.
After that, it was also recreated and retrained with quantization-aware training of TF 1.15. In this way, a fully quantized model was obtained after conversion.
* The inference was repeated several times and the average execution time was computed
* All the files required to run the test—the executable, the image files, etc.—are stored on a tmpfs RAM disk in order to make file system/storage medium overhead neglectable.
The following blocks show the execution of the classifier on the embedded platform. With the floating point model:
<pre class="board-terminal">