I would agree that your R2-UNet model is showing signs of overfitting. We can note that your model is training well considering that your training loss is consistently going down.
The easiest option is to simply save your model after every epoch and deploy early stopping using your validation loss. You can see that around epoch 7 your validation loss is at its best. You can use your model and that point and it’s possible that it performs well enough for your application.
Otherwise you will need to deploy methods to prevent overfitting. There’s many ways to go about this, you could add dropout to your model, you could make your model less complex, you could give it more data to train with, etc. It’s mainly going to be experimenting and see what works best for your model.