After performing the quantization with the ''vai_q_tensorflow'' tool and after the deployment on the target device, the model has an overall value of '''''accuracy of 93.95%'''''' and an overall weighted average '''''F1-score of 93.91%''''' on the test subset of the dataset. The model is still performing very well in correcly classify samples of the ''capacitor'' class by keeping the F1-score above 96.00% (97.03% F1-score) but, on the other hand for the remaining classes, there is a substantial drop in the value of this metric. The classes that exhibits the worst results are ''diode'' class (92.09% F1-score) and, ''IC'' class (92.06% F1-score) a low recall (88.20% recall). In general, the performance of the model is still good, similar to the one obtained with the ResNet50 model.