===ResNet50===
<gallery mode="packed" heights="300350">
File:Resnet50 train and validation accuracy.png|Train and validation accuracy trend over 1000 training epochs for ResNet50 model
File:Resnet50 train and validation loss.png|Train and validation loss trend over 1000 training epochs for ResNet50 model
</gallery>
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<gallery mode="packed" heights="240">[[File:Resnet50 host confusion matrix.png|center|Confusion matrix of ResNet50 model on host machine before quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ ResNet50 host machine classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.95740| 0.89900| 0.92728| 1000|-| style="text-align:left;" | capacitor| 0.97278| 0.96500| 0.96888| 1000|-| style="text-align:left;" | diode| 0.88558| 0.95200| 0.91759| 1000|-| style="text-align:left;" | inductor| 0.97006| 0.97200| 0.97103| 1000|-| style="text-align:left;" | resistor| 0.98882| 0.97300| 0.98085| 1000|-| style="text-align:left;" | transistor| 0.92262| 0.93000| 0.92629| 1000|- style="font-weight:bold;"! Weighted avg! 0.94954! 0.94850! 0.94865! 6000|} lorem ipsumlorem ipsumlorem ipsum [[File:Resnet50 target confusion matrix.png|center|Confusion matrix of ResNet50 model on target device after quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ ResNet50 target device classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.96384 | 0.85300 | 0.90504 | 1000|-| style="text-align:left;" | capacitor| 0.99068 | 0.95700 | 0.97355 | 1000|-| style="text-align:left;" | diode| 0.83779 | 0.94000 | 0.88596 | 1000|-| style="text-align:left;" | inductor| 0.94839 | 0.97400 | 0.96103 | 1000|-| style="text-align:left;" | resistor| 0.97211 | 0.97600 | 0.97405 | 1000|-| style="text-align:left;" | transistor| 0.89960 | 0.89600 | 0.89780 | 1000|- style="font-weight:bold;"! Weighted avg! 0.93540 ! 0.93267 ! 0.93290 ! 6000|}</gallery>
===ResNet101===
<gallery mode="packed" heights="300350">
File:Resnet101 train and validation accuracy.png|Train and validation accuracy trend over 1000 training epochs for ResNet101 model
File:Resnet101 train and validation loss.png|Train and validation loss trend over 1000 training epochs for ResNet101 model
</gallery>
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<gallery mode="packed" heights="240">[[File:Resnet101 host confusion matrix.png|center|Confusion matrix of ResNet101 model on host machine before quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ ResNet101 host machine classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.96375 | 0.95700 | 0.96036 | 1000|-| style="text-align:left;" | capacitor| 0.96373 | 0.98300 | 0.97327 | 1000|-| style="text-align:left;" | diode| 0.96425 | 0.94400 | 0.95402 | 1000|-| style="text-align:left;" | inductor| 0.98500 | 0.98500 | 0.98500 | 1000|-| style="text-align:left;" | resistor| 0.98504 | 0.98800 | 0.98652 | 1000|-| style="text-align:left;" | transistor| 0.96517 | 0.97000 | 0.96758 | 1000|- style="font-weight:bold;"! Weighted avg! 0.97116 ! 0.97117 ! 0.97112 ! 6000|} lorem ipsumlorem ipsumlorem ipsum [[File:Resnet101 target confusion matrix.png|center|Confusion matrix of ResNet101 model on target device after quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ ResNet101 target device classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.96288 | 0.88200 | 0.92067 | 1000|-| style="text-align:left;" | capacitor| 0.95898 | 0.98200 | 0.97036 | 1000|-| style="text-align:left;" | diode| 0.93965 | 0.90300 | 0.92096 | 1000|-| style="text-align:left;" | inductor| 0.93719 | 0.95500 | 0.94601 | 1000|-| style="text-align:left;" | resistor| 0.90428 | 0.99200 | 0.94611 | 1000|-| style="text-align:left;" | transistor| 0.93896 | 0.92300 | 0.93091 | 1000|- style="font-weight:bold;"! Weighted avg! 0.94033 ! 0.93950 ! 0.93917 ! 6000|}</gallery>
===ResNet152===
<gallery mode="packed" heights="300350">
File:Resnet152 train and validation accuracy.png|Train and validation accuracy trend over 1000 training epochs for ResNet152 model
File:Resnet152 train and validation loss.png|Train and validation loss trend over 1000 training epochs for ResNet152 model
</gallery>
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<gallery mode="packed" heights="240">[[File:Resnet152 host confusion matrix.png|center|Confusion matrix of ResNet152 model on host machine before quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ ResNet152 host machine classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.94553 | 0.97200 | 0.95858 | 1000|-| style="text-align:left;" | capacitor| 0.95538 | 0.98500 | 0.96997 | 1000|-| style="text-align:left;" | diode| 0.98298 | 0.92400 | 0.95258 | 1000|-| style="text-align:left;" | inductor| 0.98584 | 0.97500 | 0.98039 | 1000|-| style="text-align:left;" | resistor| 0.99390 | 0.97800 | 0.98589 | 1000|-| style="text-align:left;" | transistor| 0.92899 | 0.95500 | 0.94181 | 1000|- style="font-weight:bold;"! Weighted avg! 0.96544 ! 0.96483 ! 0.96487 ! 6000|} [[File:Resnet152 target confusion matrix.png|center|Confusion matrix of ResNet152 model on target device after quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ ResNet152 target device classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.91182 | 0.91000 | 0.91091 | 1000|-| style="text-align:left;" | capacitor| 0.94460 | 0.98900 | 0.96629 | 1000|-| style="text-align:left;" | diode| 0.96464 | 0.87300 | 0.91654 | 1000|-| style="text-align:left;" | inductor| 0.94124 | 0.94500 | 0.94311 | 1000|-| style="text-align:left;" | resistor| 0.94038 | 0.97800 | 0.95882 | 1000|-| style="text-align:left;" | transistor| 0.90358 | 0.90900 | 0.90628 | 1000|- style="font-weight:bold;"! Weighted avg! 0.93438 ! 0.93400 ! 0.93366 ! 6000|}</gallery>
===InceptionV4===
<gallery mode="packed" heights="300350">
File:InceptionV4 train and validation accuracy.png|Train and validation accuracy trend over 1000 training epochs for InceptionV4 model
File:InceptionV4 train and validation loss.png|Train and validation loss trend over 1000 training epochs for InceptionV4 model
</gallery>
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<gallery mode="packed" heights="240">[[File:InceptionV4 host confusion matrix.png|center|Confusion matrix of InceptionV4 model on host machine before quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ InceptionV4 host machine classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.94524 | 0.86300 | 0.90225 | 1000|-| style="text-align:left;" | capacitor| 0.98051 | 0.95600 | 0.96810 | 1000|-| style="text-align:left;" | diode| 0.88384 | 0.87500 | 0.87940 | 1000|-| style="text-align:left;" | inductor| 0.95575 | 0.97200 | 0.96381 | 1000|-| style="text-align:left;" | resistor| 0.96847 | 0.98300 | 0.97568 | 1000|-| style="text-align:left;" | transistor| 0.83670 | 0.91200 | 0.87273 | 1000|- style="font-weight:bold;"! Weighted avg! 0.92842 ! 0.92683 ! 0.92699 ! 6000|} lorem ipsumlorem ipsumlorem ipsum [[File:InceptionV4 target confusion matrix.png|center|Confusion matrix of InceptionV4 model on target device after quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ InceptionV4 target device classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.78158 | 0.89100 | 0.83271 | 1000|-| style="text-align:left;" | capacitor| 0.99220 | 0.89000 | 0.93832 | 1000|-| style="text-align:left;" | diode| 0.88553 | 0.82000 | 0.85151 | 1000|-| style="text-align:left;" | inductor| 0.88973 | 0.94400 | 0.91606 | 1000|-| style="text-align:left;" | resistor| 0.97319 | 0.98000 | 0.97658 | 1000|-| style="text-align:left;" | transistor| 0.83282 | 0.80700 | 0.81971 | 1000|- style="font-weight:bold;"! Weighted avg! 0.89251 ! 0.88867 ! 0.88915 ! 6000|}</gallery>
===Inception ResNet V1===
<gallery mode="packed" heights="300350">
File:Inception ResNet V1 train and validation accuracy.png|Train and validation accuracy trend over 1000 training epochs for Inception ResNet V1 model
File:Inception ResNet V1 train and validation loss.png|Train and validation loss trend over 1000 training epochs for Inception ResNet V1 model
</gallery>
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<gallery mode="packed" heights="240">[[File:Inception ResNet V1 host confusion matrix.png|center|Confusion matrix of Inception ResNet V1 model on host machine before quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ Inception ResNet V1 host machine classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.98274 | 0.96800 | 0.97531 | 1000|-| style="text-align:left;" | capacitor| 0.97571 | 0.96400 | 0.96982 | 1000|-| style="text-align:left;" | diode| 0.94889 | 0.98400 | 0.96613 | 1000|-| style="text-align:left;" | inductor| 0.98085 | 0.97300 | 0.97691 | 1000|-| style="text-align:left;" | resistor| 0.98211 | 0.98800 | 0.98504 | 1000|-| style="text-align:left;" | transistor| 0.97278 | 0.96500 | 0.96888 | 1000|- style="font-weight:bold;"! Weighted avg! 0.97385 ! 0.97367 ! 0.97368 ! 6000|} lorem ipsumlorem ipsumlorem ipsum [[File:Inception ResNet V1 target confusion matrix.png|center|Confusion matrix of Inception ResNet V1 model on target device after quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ Inception ResNet V1 target device classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score</gallery>! Support|-| style="text-align:left;" | IC| 0.84127 | 0.95400 | 0.89410 | 1000|-| style="text-align:left;" | capacitor| 0.99787 | 0.93600 | 0.96594 | 1000|-| style="text-align:left;" | diode| 0.94346 | 0.90100 | 0.92174 | 1000|-| style="text-align:left;" | inductor| 0.95275 | 0.98800 | 0.97005 | 1000|-| style="text-align:left;" | resistor| 0.94852 | 0.99500 | 0.97121 | 1000|-| style="text-align:left;" | transistor| 0.93348 | 0.82800 | 0.87758 | 1000|- style="font-weight:bold;"! Weighted avg! 0.93622 ! 0.93367 ! 0.93344 ! 6000|}
===Inception ResNet V2===
<gallery mode="packed" heights="300350">
File:Inception ResNet V2 train and validation accuracy.png|Train and validation accuracy trend over 1000 training epochs for Inception ResNet V2 model
File:Inception ResNet V2 train and validation loss.png|Train and validation loss trend over 1000 training epochs for Inception ResNet V2 model
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<gallery mode="packed" heights="240">[[File:Inception ResNet V2 host confusion matrix.png|center|Confusion matrix of Inception ResNet V2 model on host machine before quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ Inception ResNet V2 host machine classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score! Support|-| style="text-align:left;" | IC| 0.97872 | 0.96600 | 0.97232 | 1000|-| style="text-align:left;" | capacitor| 0.99177 | 0.96400 | 0.97769 | 1000|-| style="text-align:left;" | diode| 0.98963 | 0.95400 | 0.97149 | 1000|-| style="text-align:left;" | inductor| 0.97931 | 0.99400 | 0.98660 | 1000|-| style="text-align:left;" | resistor| 0.98213 | 0.98900 | 0.98555 | 1000|-| style="text-align:left;" | transistor| 0.93365 | 0.98500 | 0.95864 | 1000|- style="font-weight:bold;"! Weighted avg! 0.97587 ! 0.97533 ! 0.97538 ! 6000|} lorem ipsumlorem ipsumlorem ipsum [[File:Inception ResNet V2 target confusion matrix.png|center|Confusion matrix of Inception ResNet V2 model on target device after quantization]] {| class="wikitable" style="margin: auto; text-align: center;"|+ Inception ResNet V2 target device classification report|- style="font-weight:bold;"! Class! Precision! Recall! F1-score</gallery>! Support|-| style="text-align:left;" | IC| 0.91735 | 0.89900 | 0.90808 | 1000|-| style="text-align:left;" | capacitor| 0.99466 | 0.93200 | 0.96231 | 1000|-| style="text-align:left;" | diode| 0.98793 | 0.90000 | 0.94192 | 1000|-| style="text-align:left;" | inductor| 0.92066 | 0.99800 | 0.95777 | 1000|-| style="text-align:left;" | resistor| 0.96970 | 0.99200 | 0.98072 | 1000|-| style="text-align:left;" | transistor| 0.87887 | 0.93600 | 0.90654 | 1000|- style="font-weight:bold;"! Weighted avg! 0.94486 ! 0.94283 ! 0.94289 ! 6000|}
==Comparison==
==Useful links==
*Young-Gyu Kim, Tae-Hyoung Park, [https://res.mdpi.com/d_attachment/applsci/applsci-10-04598/article_deploy/applsci-10-04598.pdf ''SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions''], July 2020.
*Hangwei Lu, Dhwani Mehta, Olivia Paradis, Navid Asadizanjani, Mark Tehranipoor, Damon L. Woodard, [https://eprint.iacr.org/2020/366.pdf ''FICS-PCB: A Multi-Modal Image Dataset for Automated Printed Circuit Board Visual Inspection''], July 2020.
*Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, [https://arxiv.org/pdf/1512.03385.pdf ''Deep Residual Learning for Image Recognition''], December 2015.
*Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi, [https://arxiv.org/pdf/1602.07261.pdf,''Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning''], August 2016.
