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4.3. Evaluation on Real Rainy Images #4

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qiyinhe opened this issue May 27, 2019 · 4 comments
Open

4.3. Evaluation on Real Rainy Images #4

qiyinhe opened this issue May 27, 2019 · 4 comments

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@qiyinhe
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qiyinhe commented May 27, 2019

您好作者,请问文件夹 ./PReNet/logs/real/中的两组参数PReNet1.pth和PReNet2.pth是在哪个数据集上训练出来的?

@csdwren
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csdwren commented May 28, 2019

Rain100H、Rain100L和Rain1400的训练集都用了,但是因为Rain1400的训练集图像数量远多于其他两个,所以在构建h5文件的时候,stride是其他两个训练集的3倍。另外,PReNet多个epoch之后会在训练集上过拟合,1个epoch就停止训练的模型在真实图的扩展能力反而更好。PReNet1.pth和PReNet2.pth都是在这3个训练集的1个epoch的模型,区别只是recursive loss 和 final loss的区别,视觉效果应该基本没有区别。

@githubPsearth
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Rain100H、Rain100L和Rain1400的训练集都用了,但是因为Rain1400的训练集图像数量远多于其他两个,所以在构建h5文件的时候,stride是其他两个训练集的3倍。另外,PReNet多个epoch之后会在训练集上过拟合,1个epoch就停止训练的模型在真实图的扩展能力反而更好。PReNet1.pth和PReNet2.pth都是在这3个训练集的1个epoch的模型,区别只是recursive loss 和 final loss的区别,视觉效果应该基本没有区别。

作者您好,请问是将三个训练集混合之后,进行训练吗?

@csdwren
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csdwren commented Jun 23, 2019

对。只用rain100H训练集应该也可以,不过不能训练太充分,过拟合之后对真实图效果就很差了。

@SunshineJunFu
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Could you provide your deraining results on real-world rain images?

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