Closed alliegu closed 1 month ago
Oh nevermind, it's just the encoder
Oh nevermind, it's just the encoder
No worries! Please let me know if you need further explanation. If you manage to find out what the problem is, I'm willing to spend GPU time to retrain this and share the checkpoints.
Can I ask what python / pytorch / torchvision versions you're using? I did the installation steps but ran into versioning issues; downgraded and new issues popped up. Thank you so much!
Can I ask what python / pytorch / torchvision versions you're using? I did the installation steps but ran into versioning issues; downgraded and new issues popped up. Thank you so much!
Whops, sorry I missed this reply. @alliegu I'm using Python 3.10, torch 2.0.1 and the related torchvision version. See here.
Just wanted to comment that with your pretrained full checkpoint, even though I couldn't reproduce correct predictions using test samples in the paper, this is a sufficiently good feature extractor for my use case on unlabeled fonts. Really appreciate it!
Just wanted to comment that with your pretrained full checkpoint, even though I couldn't reproduce correct predictions using test samples in the paper, this is a sufficiently good feature extractor for my use case on unlabeled fonts. Really appreciate it!
@anruigu I'm glad you found this helpful! Keep up the great work!
Hi! Testing this implementation on real images and trying to figure out if I can improve performance. In the paper they mention that Layers 1 and 2 are imported to the CNN in Fig. 5, as the Cu sub-network and fixed. But in the implementation you seem to call the full autoencoder upon training the full network. I wonder if that was intentional? Thanks! Edit: nevermind, it's just the encoder