Train locally on your own machine if you have a GPU, or try using google colab if you don't. Google Colab has V100 which should fit larger batch sizes.

To be honest, given how limited the data set is and how small some of the segmentation masks are, I am not sure other architectures will be able to do any better than U-Net.

I would also try the nnU-Net which should give state-of-the-art performance, and so will give you a good idea of what's possible with the dataset that you have: https://github.com/MIC-DKFZ/nnUNet

Accuracy is not a good metric here given the large number of true negative pixels you will get.

How large is the typical region you are trying to segment (in pixels)? If you've already done data augmentation I would also try to generate images if you can. Use a larger batch size, try different optimizers and a learning rate scheduler. How many images do not have cavities in them?

What accuracy (Dice?) are you getting? 100 training images is pretty small. Have you tried cross-validation?

Yes it's worth using.

What's Upwork?

Nm you?

Gottem

Attention is all you want, but not all you need

It's best use is as an assistant, provided it is accurate. So far in my tests it has been pretty good at writing boilerplate code and email/letter templates. Better than githubs copilot for code.

Automatic1111's webui runs in linux or windows

paging Automatic1111

pls implement in webui

No, the problem is impairing them