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 on the capacitor class by keeping a F1-score above 96.00% in three classes i.e ''resistor'' (97.12% F1-score), ''inductor'' (97.00% F1-score) and, ''capacitor'' (96.59% F1-score)by keeping a F1-score above 96.00%. However, for the remaining classes, the value of the metric is substantially reduced. The classes that exhibit the worst results are ''IC'' (89.41% F1-score) due to having low precision (84.12% precision) and, ''transistor'' (87.75% F1-score) due to having a very low recall (82.80% recall). In general, the performance of the model is still good, similar to the one obtained with ResNet models.
lorem ipsumThe model, before performing the quantization with the vai_q_tensorflow tool, has an overall value of '''''accuracy of 97.53%''''' and an overall weighted average '''''F1-score of 97.53%''''' over the test subset of the dataset, showing a very high generalization capability on unseen samples. Five classes have a F1-score above 96.00%, actually very high for class ''inductor'' (98.66% F1-score)lorem ipsumlorem ipsumand, class ''resistor'' (98.55% F1-score). The worst result is the one displayed by the class ''transistor'' by having a F1-score below 96.00% but, still very close (95.86% F1-score) mainly due to a low value of the precision metric (93.36% precision).
lorem ipsumlorem ipsumlorem ipsumThe 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).