Retinal Vessel Segmentation for Retinography

		driu	hed	m2unet	unet
Dataset	2nd. Annot.	15M	14.7M	0.55M	25.8M
drive	0.788 (0.021)	0.768 (0.031)	0.750 (0.036)	0.771 (0.027)	0.775 (0.029)
stare	0.759 (0.028)	0.786 (0.100)	0.738 (0.193)	0.800 (0.080)	0.806 (0.072)
chasedb1	0.768 (0.023)	0.778 (0.031)	0.777 (0.028)	0.776 (0.031)	0.779 (0.028)
hrf		0.742 (0.049)	0.719 (0.047)	0.735 (0.045)	0.746 (0.046)
iostar-vessel		0.790 (0.023)	0.792 (0.020)	0.788 (0.021)	0.783 (0.019)

Notes

• The following table describes recommended batch sizes for 24Gb of RAM GPU card, for supervised training of COD-systems:

  # change <model> and <dataset> by one of items bellow
  $ bob binseg experiment -vv <model> <dataset> --batch-size=<see-table> --device="cuda:0"
  

  Models / Datasets	drive-covd	stare-covd	chasedb1-covd	iostar-vessel-covd	hrf-covd
  driu / driu-bn	4	4	2	2	2
  m2unet	8	4	4	4	4