  Quality inspection has become crucial in any large-scale manufacturing
industry recently. In order to reduce human error, it has become imperative to
use efficient and low computational AI algorithms to identify such defective
products. In this paper, we have compared and contrasted various pre-trained
and custom-built architectures using model size, performance and CPU latency in
the detection of defective casting products. Our results show that custom
architectures are efficient than pre-trained mobile architectures. Moreover,
custom models perform 6 to 9 times faster than lightweight models such as
MobileNetV2 and NasNet. The number of training parameters and the model size of
the custom architectures is significantly lower (~386 times & ~119 times
respectively) than the best performing models such as MobileNetV2 and NasNet.
Augmentation experimentations have also been carried out on the custom
architectures to make the models more robust and generalizable. Our work sheds
light on the efficiency of these custom-built architectures for deployment on
Edge and IoT devices and that transfer learning models may not always be ideal.
Instead, they should be specific to the kind of dataset and the classification
problem at hand.
