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 in correcly classify samples of belonging to 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 its measured 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).

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 belonging to the ''capacitor'' class by keeping the F1-score above 96.00% (97.03% 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 (92.09% F1-score) and, ''IC'' class (92.06% F1-score) because both class shows a low value of the recall metric (90.30% recall for the former, 88.20% recall for the latter). 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 93.40%''''' 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 classify samples of 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'' (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.

The model, before performing the quantization with the vai_q_tensorflow tool, has an overall value of '''''accuracy of 92.68%''''' and an overall weighted average '''''F1-score of 92.69%''''' over the test subset of the dataset, showing a good generalization capability on unseen samples, although lower than in the three ResNet models. The classes with the highest F1-score, above 96.00% are: ''resistor'' (97.56% F1-score), ''capacitor'' (96.81% F1-score) and, ''inductor'' (96.38% F1-score). However, the model performance on the classification task in the three remaining classes is poorly compared w.r.t the previous models, showing an F1-score below 90.00% in the ''diode'' class (87.94% F1-score) and, ''transistor'' class (87.27% F1-score) because both classes have a low value in precision and recall metrics for the former (88.38% precision, 87.50% recall) and, a low value in precision metric for the latter (83.67% 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 88.87%''''' and an overall weighted average '''''F1-score of 88.91%''''' on the test subset of the dataset. The model is still performing well on in correcly classify samples belonging to ''resistor'' class (97.65% F1-score) but, on . On the other hand for the remaining classes, there is a substantial drop reduction in the value of the this metric. The classes that exhibit the worst results are ''diode'' class (85.15% F1-score), ''IC'' (83.27% F1-score) and, ''transistor'' class (81.97% F1-score). In general, the performance of the model is still good, but it is decidedly lower than the one obtained with the ResNet models analyzed previously.