  Training deep neural networks (DNNs) takes signifcant time and resources. A
practice for expedited deployment is to use pre-trained deep neural networks
(PTNNs), often from model zoos -- collections of PTNNs; yet, the reliability of
model zoos remains unexamined. In the absence of an industry standard for the
implementation and performance of PTNNs, engineers cannot confidently
incorporate them into production systems. As a first step, discovering
potential discrepancies between PTNNs across model zoos would reveal a threat
to model zoo reliability. Prior works indicated existing variances in deep
learning systems in terms of accuracy. However, broader measures of reliability
for PTNNs from model zoos are unexplored. This work measures notable
discrepancies between accuracy, latency, and architecture of 36 PTNNs across
four model zoos. Among the top 10 discrepancies, we find differences of
1.23%-2.62% in accuracy and 9%-131% in latency. We also fnd mismatches in
architecture for well-known DNN architectures (e.g., ResNet and AlexNet). Our
findings call for future works on empirical validation, automated tools for
measurement, and best practices for implementation.

Software Engineering;Artificial Intelligence;Machine Learning