  Structural pruning has emerged as a promising approach for producing more
efficient models. Nevertheless, the community suffers from a lack of
standardized benchmarks and metrics, leaving the progress in this area not
fully comprehended. To fill this gap, we present the first comprehensive
benchmark, termed \textit{PruningBench}, for structural pruning. PruningBench
showcases the following three characteristics: 1) PruningBench employs a
unified and consistent framework for evaluating the effectiveness of diverse
structural pruning techniques; 2) PruningBench systematically evaluates 16
existing pruning methods, encompassing a wide array of models (e.g., CNNs and
ViTs) and tasks (e.g., classification and detection); 3) PruningBench provides
easily implementable interfaces to facilitate the implementation of future
pruning methods, and enables the subsequent researchers to incorporate their
work into our leaderboards. We provide an online pruning platform
http://pruning.vipazoo.cn for customizing pruning tasks and reproducing all
results in this paper. Codes will be made publicly on
https://github.com/HollyLee2000/PruningBench.
