I’m training a binary segmentation model to identify the background and foreground of a surgical image. The definition of the learner is as follows:
learn = unet_learner(dls, resnet34, self_attention=True, metrics=custom_accuracy).to_fp16()
When I run the
learn.lr_find() method, I get the following line shape:
Ideally, the shape should look like the following line where it gradually goes down and then shoots up:
There are 24,000 images in the training set and the
32. I think the data volume might not the problem but don’t know what else could impact the determination of a stable
lr for binary segmentation. Any ideas for things to further investigate this issue?
I will appreciate any hints.
Many thanks and