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Hi, the loss function in train() and Loss() have a different number of parameters. (5 params vs 6 params)
Besides, the Loss_target() Class is also missing (in train.py). Are you using a different loss lib other than the provided one? Thanks!
Thanks for your amazing work, When the trained weights will be available to test?
Hi authors,
Thank you for your great work. Will you be releasing the training code? It's been "TBD" for quite a while.
Best,
你好,请教您一个问题:如何利用ATA方法去学习域无关的特征呢?就是论文中的第三个公式是怎么实现的呢?
It's a amazing model and I want to train it with new language. How can I do that?
SyntoReal_STD/dataset/ICDAR15.py
Line 330 in 02ee312
Sorry but I think this is really not a code that can be followed up with. In this line, the data loader for ICDAR15 masked all the predictions which troubled me for a long while :(
I'd recommend followers wait for further updates or bug fixes from the author, otherwise, you may run into random logic bugs. If you insist, be reminded to fix the above prob.
Besides, I will also release a related but not exactly re-implement of syn2real later.
Thank you for your great work!
In the training file, 'network/loss_target.py' is needed to to import Loss_target, but i cannot locate it.
Would you update that part?
Thanks.
为什么不在每个训练epoch中更新pseduo label ?
SyntoReal_STD/trainSyndataToICDAR15.py
Lines 138 to 143 in 02ee312
可以改成以下这样吗?
for epoch in range(args.epoch_iter):
generate_pseduo(model, args.target_image, args.target_pseudo_positive, args.target_pseudo_negative, device)
train(epoch, model, optimizer, train_loader_source, train_loader_target)
Thank you for your work . The paper is great and i want to test model but i can't download baidu server. Please upload weight on google driver . Thank a lot
Dear author, could you please release the complete code? Thanks!!!!!!!!!!!!!!!!!!
Hi @weijiawu Mr Weijia Wu, thanks for your work and code sharing!
May I know what is the source/target domain for this pre-trained model? (e.g. Synthtext to ICDAR 15 or 13?) I can not re-produce your results in the paper.
Thanks for any useful information!
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