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.

翻译：结构剪枝已成为一种生成更高效模型的有前景的方法。然而，该领域缺乏标准化的基准测试与评估指标，导致该方向的进展未能被充分理解。为填补这一空白，我们提出了首个用于结构剪枝的综合基准测试，称为 \textit{PruningBench}。PruningBench 展现出以下三个特点：1) PruningBench 采用统一且一致的框架来评估多种结构剪枝技术的有效性；2) PruningBench 系统性地评估了 16 种现有剪枝方法，涵盖了广泛的模型（例如 CNN 和 ViT）与任务（例如分类与检测）；3) PruningBench 提供了易于实现的接口，以促进未来剪枝方法的实施，并使后续研究者能够将其工作纳入我们的排行榜。我们提供了一个在线剪枝平台 http://pruning.vipazoo.cn，用于定制剪枝任务并复现本文所有结果。代码将公开于 https://github.com/HollyLee2000/PruningBench。