Over the past few years, there has been significant interest in video action recognition systems and models. However, direct comparison of accuracy and computational performance results remain clouded by differing training environments, hardware specifications, hyperparameters, pipelines, and inference methods. This article provides a direct comparison between fourteen off-the-shelf and state-of-the-art models by ensuring consistency in these training characteristics in order to provide readers with a meaningful comparison across different types of video action recognition algorithms. Accuracy of the models is evaluated using standard Top-1 and Top-5 accuracy metrics in addition to a proposed new accuracy metric. Additionally, we compare computational performance of distributed training from two to sixty-four GPUs on a state-of-the-art HPC system.

中文翻译：

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视频动作识别方法的准确性和性能比较

在过去的几年里，人们对视频动作识别系统和模型产生了浓厚的兴趣。然而，由于不同的训练环境、硬件规格、超参数、管道和推理方法，准确度和计算性能结果的直接比较仍然模糊不清。本文通过确保这些训练特征的一致性，对 14 个现成模型和最先进模型进行了直接比较，以便为读者提供不同类型的视频动作识别算法的有意义的比较。除了建议的新准确度指标外，还使用标准 Top-1 和 Top-5 准确度指标评估模型的准确度。此外，我们在最先进的 HPC 系统上比较了 2 到 64 个 GPU 的分布式训练的计算性能。