Comments (1)
您好:
关于config,我所报告的实验结果是基于现在仓库代码中的optimizer的训练参数所得到的(受限于机器原因可能在Batch_size和lr上有所调整),应该在代码中都能看得到。
实际上在最初实验的时候我有试过SGD去训练,使用论文中的参数得到的结果并不如Adam。并且作者没有给出余弦退火的超参设置,我试了一组默认参数效果也并不好,所以没有完全按照论文中给出的参数训练。
实际上代码的实现上也不能保证与原论文一致,所以我report的结果仅供参考。
关于evaluate的方法确实正如您所说,shuffle后每个视频取50帧。这样的作法是为了平衡和加速。测试集上的每个视频帧数并不相同,为了得到一个相对公平的结果就对每个视频的帧数进行了控制,使得各个视频占比一致。
取50帧是为了尽可能加速evaluate,因为在我的程序里每过一段时间就会运行evaluate来输出一次结果,我希望这个函数不要占用太多时间。
不过取得帧数较少确实可能会导致结果不稳定,如果希望得到更准确的结果可以修改evaluate函数里的frame_num参数。
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