How to run model inference for classification using test images on PC ? Hi,I've been trying to set up an experiment that deploys the MobileNet v2 model from the STM32AI Model Zoo (mobilenetv2_a035_128_fft_int8.tflite) to do some classification of images using my own test dataset; so that's a set of discrete images, not real-time camera data. I want to use a Python script on a PC to send each test image, one at a time, to the MCU via serial (USB C), have the model run inference, and then return the classification results to the PC. This will repeat in a loop so all images are processed and the Python script will then calculate final metrics, based on all the individual image classification results.I already have some Python code and can send an image as a tensor to the MCU, but I am struggling to correctly return, or interpret, the classification results. I.e., using the model trained on the Flowers dataset, I expect a classification result consisting of 5 float values (one for each class in the dataset) to be returned. But the values I get
