  There is a lot of ongoing research effort into developing different
techniques for neural networks compression. However, the community lacks
standardised evaluation metrics, which are key to identifying the most suitable
compression technique for different applications. This paper reviews existing
neural network compression evaluation metrics and implements them into a
standardisation framework called NetZIP. We introduce two novel metrics to
cover existing gaps of evaluation in the literature: 1) Compression and
Hardware Agnostic Theoretical Speed (CHATS) and 2) Overall Compression Success
(OCS). We demonstrate the use of NetZIP using two case studies on two different
hardware platforms (a PC and a Raspberry Pi 4) focusing on object
classification and object detection.
