About training with width-3

[MikeXuQ]

Hello, thanks for providing the code. I am really interested in the work. I want to make the line generated by the model thinner. So I trained it as you inform. And I changed the aug_folder from width-5 to width-3. The hyper-parameter I used will appear in the end. But after training, the model can just generate a picture containing nothing. Can you help me solve this problem?
python train.py
--name width3
--dataroot ${dataDir}/ContourDrawing/
--checkpoints_dir ${dataDir}/Photosketch/Checkpoints/
--model pix2pix
--which_direction AtoB
--dataset_mode 1_to_n
--no_lsgan
--norm batch
--pool_size 0
--output_nc 1
--which_model_netG resnet_9blocks
--which_model_netD global_np
--batchSize 2
--lambda_A 200
--lr 0.0002
--aug_folder width-3
--crop --rotate --color_jitter
--niter 400
--niter_decay 400 \

[mtli]

TL;DR: you can try to thin the output in post-processing instead of re-training the model.

Hi, thanks for your interest. The width of the sketch is a tricky problem. First of all, training with width-3 is feasible, but might require a different lambda_A. I suggest you train first with the default width-5 for sanity check. Second, we use width-5 to train the model is because width-5 provides stronger supervision signal and thus better performance. If you want to get the same performance as width-5, you might want to consider sample balancing techniques used in boundary detection, e.g. a weight the loss by the inverse of the ratio between foreground and background pixels. Coming back to your task of getting thinner output, I would suggest you to apply some thinning operations in the post-processing step instead of re-training the model, for example, check out bwmorph.

[MikeXuQ]

I get it! Thanks for your reply!