  With the rapid advances in deep learning and smart manufacturing in Industry
4.0, there is an imperative for high-throughput, high-performance, and fully
integrated visual inspection systems. Most anomaly detection approaches using
defect detection datasets, such as MVTec AD, employ one-class models that
require fitting separate models for each class. On the contrary, unified models
eliminate the need for fitting separate models for each class and significantly
reduce cost and memory requirements. Thus, in this work, we experiment with
considering a unified multi-class setup. Our experimental study shows that
multi-class models perform at par with one-class models for the standard MVTec
AD dataset. Hence, this indicates that there may not be a need to learn
separate object/class-wise models when the object classes are significantly
different from each other, as is the case of the dataset considered.
Furthermore, we have deployed three different unified lightweight architectures
on the CPU and an edge device (NVIDIA Jetson Xavier NX). We analyze the
quantized multi-class anomaly detection models in terms of latency and memory
requirements for deployment on the edge device while comparing
quantization-aware training (QAT) and post-training quantization (PTQ) for
performance at different precision widths. In addition, we explored two
different methods of calibration required in post-training scenarios and show
that one of them performs notably better, highlighting its importance for
unsupervised tasks. Due to quantization, the performance drop in PTQ is further
compensated by QAT, which yields at par performance with the original 32-bit
Floating point in two of the models considered.
