datasets.rst

Supported Datasets

#	Name	H x W	# imgs	Train	Test	Mask	Vessel	OD	Cup	Train-Test split reference
1	Drive_	584 x 565	40	20	20	x	x			`Staal et al. (2004)`_
2	STARE_	605 x 700	20	10	10		x			`Maninis et al. (2016)`_
3	CHASEDB1_	960 x 999	28	8	20		x			`Fraz et al. (2012)`_
4	HRF_	2336 x 3504	45	15	30	x	x			`Orlando et al. (2016)`_
5	IOSTAR_	1024 x 1024	30	20	10	x	x	x		`Meyer et al. (2017)`_
6	DRIONS-DB_	400 x 600	110	60	50			x		`Maninis et al. (2016)`_
7	RIM-ONEr3_	1424 x 1072	159	99	60			x	x	`Maninis et al. (2016)`_
8	Drishti-GS1_	varying	101	50	51			x	x	`Sivaswamy et al. (2014)`_
9	REFUGE_ train	2056 x 2124	400	400				x	x	REFUGE_
9	REFUGE_ val	1634 x 1634	400		400			x	x	REFUGE_

Add-on: Folder-based Dataset

For quick experimentation, we also provide a PyTorch_ class that works with the following dataset folder structure for images and ground-truth (gt):

root
   |- images
   |- gt

The file names should have the same stem. Currently, all image formats that can be read via PIL are supported. Additionally, we also support HDF5 binary files.

For training, a new dataset configuration needs to be created. You can copy the template :ref:`bob.ip.binseg.configs.datasets.imagefolder` and amend it accordingly, e.g. to point to the the full path of the dataset and if necessary any preprocessing steps such as resizing, cropping, padding etc.

Training can then be started with, e.g.:

bob binseg train M2UNet /path/to/myimagefolderconfig.py -b 4 -d cuda -o /my/output/path -vv

Similary for testing, a test dataset config needs to be created. You can copy the template :ref:`bob.ip.binseg.configs.datasets.imagefoldertest` and amend it accordingly.

Testing can then be started with, e.g.:

bob binseg test M2UNet /path/to/myimagefoldertestconfig.py -b 2 -d cuda -o /my/output/path -vv