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.

翻译：当前大量研究工作致力于开发神经网络压缩的不同技术。然而，该领域缺乏标准化的评估指标，而这些指标对于为不同应用场景确定最合适的压缩技术至关重要。本文综述了现有的神经网络压缩评估指标，并将其整合到名为NetZIP的标准化框架中。我们提出了两种新型指标以填补文献中现有评估的空白：1）压缩与硬件无关的理论速度（CHATS）和2）总体压缩成功率（OCS）。我们通过在两种不同硬件平台（PC和树莓派4）上聚焦目标分类与目标检测的两个案例研究，展示了NetZIP的应用效果。