Comments (7)
可以参考这个文件2DCNN-example,这个用的是重建的版本,重建过程在dataset类内,只是一个简单的逆向累加
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哦,40GB的版本不是积分的版本,是将event list转换为帧存储的版本
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还是有些没搞明白,在readme中提到的converted event-frame version(40G),和这里重建的版本有什么区别呢
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这里只是因为有一位同学试了试把原本用四元组存储的数据预先恢复成了矩阵存储,可能是事件率偏高或者pytorch有存储优化,数据集尺寸小了很多,所以也上传上来了
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嗯嗯,如果我想用2DCNN-example来跑Baseline,我可以不进行原始数据集的重构,直接用event-frame version的数据集嘛
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理论上可以用event-frame version仿照这个example里的dataset的方式进行一些预处理恢复成2D的灰度数据,这个version每个样本应该是2通道,8帧。
*****其实我自己也没有检查过它的准确性,但据说这个版本训练会快很多。
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明白了~感谢你的回复和分享你们的工作
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