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 93.40%''''' and an overall weighted average '''''F1-score of 93.36%''''' on the test subset of the dataset. The model is still performing very well on ''capacitor'' class by keeping a F1-score above 96.00% (96.62% F1-score) but, on the other hand, for the remaining classes there is a substantial drop in the value of the metric. The classes that exhibit the worst results are ''diode'' (91.65% F1-score) because the recall is very low (87.30% recall), ''IC'' (91.09% F1-score) having low precision and recall (91.18% precision, 91.00% recall) and, ''transistor'' (90.62% F1-score) having low precision and recall (90.35% precision, 90.62% recall). In general, the performance of the model is still good, similar to the one obtained with two previous models, especially similar to the ResNet101 model.