Fusing the pre-processing operators for efficient deployment

In this last recipe, we will develop a sketch to classify desk objects with the Arduino Nano. However, the ML deployment is not the only thing we must take care of. Indeed, a few additional operations must be implemented to supply the correct input image to the model.

Therefore, in this recipe, we will not just discuss model deployment but also delve into implementing a memory-efficient pre-processing pipeline, preparing the input for the model.

Getting ready

RAM usage is impacted by the variables allocated during the program execution, such as the input, output, and intermediate tensors of the ML model. However, the model is not solely responsible for memory utilization. In fact, the image acquired from the OV7670 camera needs to be pre-processed with the following operations to provide the appropriate input to the model:

• Image cropping to match the input shape aspect ratio of the model
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