RCAN
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请教一下训练细节
您好!请问一下,对于每次的迭代,是从DIV2K中随机抽取16张图像,然后随机裁剪出48x48的patch,最后执行8个方向的变换,获得训练数据吗?DIV一共有800张图像,那么一个epoch应该是800/16=50次迭代,您论文中是每200000次迭代学习率下降一半,也就是说是每4000个epoch才下降一次学习率吗?但是您的代码中默认是每200次迭代下降一次学习率,并且整个训练也只有1000次迭代。而按照1000个epoch训练下来,set5的PSNR只能达到32.2。可能我在训练细节上的理解与您有出入,还请不吝赐教。 由于pytorch版本升级,我重写了您的数据集代码,因此细节未知。
you can check the code in this so in training process, the training data will be repeated 20 times. 800 images will be 800*20=16000 in one epoch, in other words, 16000/16=1000 iters. training of 200 epochs is exactly equal to 200000 iters
you can check the code in this so in training process, the training data will be repeated 20 times. 800 images will be 800*20=16000 in one epoch, in other words, 16000/16=1000 iters. training of 200 epochs is exactly equal to 200000 iters
Thank you very much~
you can check the code in this so in training process, the training data will be repeated 20 times. 800 images will be 800*20=16000 in one epoch, in other words, 16000/16=1000 iters. training of 200 epochs is exactly equal to 200000 iters
Thank you very much~
您好,请问您从头开始训练时,有遇到模型不收敛的问题吗?我的loss一直在1e29附近振荡
@kongdebug 你大概训练了多少轮?