More details about training

[Sanster]

Thanks for sharing the code and dataset. The encoder-only architecture makes DDCP faster and lighter than other methods, I really like the idea. I try to reimplement the paper, however, some training details are missing in the paper.

loss

1. What are α and β used for the pre-train model? In the utilsV4.py it's all equal 1

Experiments

2. What are the total epochs for the pre-train model? In train.py, the default epochs=300

[gwxie]

Hi, 1、please see here.https://github.com/gwxie/Document-Dewarping-with-Control-Points/blob/ba4639ff1a0c8884d549e7fae3a9bddf1bef14ab/Source/train.py#L116 2、We have printed the EPOCH of pre-train model. see here https://github.com/gwxie/Document-Dewarping-with-Control-Points/blob/ba4639ff1a0c8884d549e7fae3a9bddf1bef14ab/Source/train.py#L93

[Sanster]

Thanks for your response!

Have you tried adding a semantic segmentation head? I tried to add an encoder to predict document mask, but the network does not converge.

[gwxie]

Thanks for your response!

Have you tried adding a semantic segmentation head? I tried to add an encoder to predict document mask, but the network does not converge.

Hi， I've never done anything like this before.