Changes

The model, before performing the quantization with the ''vai_q_tensorflow'' tool, has an overall value of '''''accuracy of 96.46%''''' and an overall weighted average '''''F1-score of 96.48%''''' over the test subset of the dataset, showing a good generalization capability on unseen samples. The classes with the highest F1-score, above 96.00% are respectively ''resistor'' class (98.58% F1-score), ''inductor'' class (98.03% F1-score) and, ''capacitor'' class (96.99% F1-score). The worst performance is the one displayed by the ''transistor'' class by having "only" a F1-score around 94.00% (94.18% F1-score) mainly bacause because the model exhibits a low value of the precision metric in this class (92.89% precision).

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 in correcly correctly classify samples belonging to the ''capacitor'' class by keeping a F1-score above 96.00% (96.62% F1-score). On the other hand , for the remaining classes, there is a substantial reduction in the value of this metric. The classes that exhibit the worst results are ''diode'' class (91.65% F1-score) because the value of the recall metric is very low (87.30% recall), ''IC'' class (91.09% F1-score) by having a low value measured for both precision and recall metrics (91.18% precision, 91.00% recall) , and, ''transistor'' class (90.62% F1-score) having a low value of precision and recall (90.35% precision, 90.62% recall) in the same way as the previous case. In general, the performance of the model is still good, similar to the performance obtained with two previous models, especially to the one of ResNet101 model.