Implementation for OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation (IEEE Transactions on Biomedical Engineering))
Thank you for your extraordinary contributions. I have a few questions. There are too many "train_xxx.py" files here. If I only want to perform retinal vessel segmentation tasks using my own dataset, which "train" code should I use? Also, my experimental server does not have resource scheduling tools like "slurm". How should I make a modification to the train script to use LocalExecutory?
Please let me know if you need any further assistance with this issue.
Thank you for your wonderful work. But I got some questions for you. Could you please show us in detail how to train this model? For example, which folder should I put the dataset in? Or which file should I run to start training?
Thank you again, I am looking forward to your response!
Need to add a logging method that supports image logging in training with Lightning.
Discussion
Logging is needed. Normally we can just use Lightning's built-in dashboard as usual. However, for Image logging for attention visualization. We need to convert the tensor into a PIL image. In which, I'm not sure about performance impact.
I'm currently considering the usage of visdom logger. But I'm not sure we can easily integrate it with lightning though.