Changes

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 93.27%''''' and an overall weighted average '''''F1-score of 93.29%''''' on the test subset of the dataset. The model is still performing well on in correcly classify samples of the ''resistor'' class (98.08% F1-score), ''inductor'' class(97.10% F1-score) and, ''capacitor'' class (96.88% F1-score). The worst results of the model in the classification task can be found in the ''transistor'' class (89.78% F1-score) because both precision and recall metrics are below 90.00% (89.96% precision and, 89.60% recall) and, in the ''diode'' class (88.59% F1-score) because the precision metric is very low (83.77% precision).

The model, before performing the quantization with the ''vai_q_tensorflow'' tool, has an overall value of '''''accuracy of 97.10%''''' and an overall weighted average '''''F1-score of 97.11%''''' over the test subset of the dataset, showing a very high generalization capability on unseen samples. All the classes have a F1-score above 96.00%, in . In particular it is very high in the ''resistor'' class (98.65% F1-score) and, in the ''inductor'' class (98.50% F1-score) with the . The only exception of is the ''diode'' class (95.40% F1-score) mainly because it has a low value of recall metric (94.40% recall).

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 . On the other hand for the remaining classes, there is a substantial drop reduction in the value of this metric. The classes that exhibits exhibit 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.

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 93.34%' and an overall weighted average F1-score of 93.34% on the test subset of the dataset. The model is still performing very well in two classes i.e ''resistor'' (98.07% F1-score) and, ''capacitor'' (96.23% F1-score) by keeping a F1-score above 96.00%. However, for the remaining classes, the value of the metric is reduced. In particular the worst results can be found in the class ''IC'' (90.80% F1-score) by having a low value for precision and recall metrics (91.73% precision, 89.90% recall) and, class ''transistor'' due to have low precision (87.88% precision).