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pretrained networks for deep learning applications

Version 19 2021-03-06, 11:34
Version 18 2021-03-06, 11:33
Version 17 2020-12-16, 14:32
Version 16 2020-11-12, 20:59
Version 15 2020-11-11, 17:47
Version 14 2020-10-04, 17:10
Version 13 2020-10-04, 14:35
Version 12 2020-10-04, 12:37
Version 11 2020-10-04, 11:34
Version 10 2020-04-21, 11:43
Version 9 2020-04-21, 10:30
Version 8 2020-04-20, 14:52
Version 7 2020-04-20, 14:06
Version 6 2020-04-19, 16:38
Version 5 2020-03-28, 11:38
Version 4 2020-03-28, 10:51
Version 3 2019-01-14, 11:50
Version 2 2019-01-13, 14:21
Version 1 2018-10-24, 13:41
dataset
posted on 2021-03-06, 11:34 authored by Brian AvantsBrian Avants, Nick TustisonNick Tustison
Pretrained networks for a variety of problems.

WBIR networks are named wbir*

put them all in a folder called "models" then see create_wbir_models.R for how to load them

wbir_random_vgg_3d --- 3dvgg initialized with random weights


wbir_resnet_vgg_weights --- resnet trained to mimic 2d pseudo vgg activations


wbir_vggtrue3d -- 3d egg trained to mimic 2d pseudo vgg activations


wbir_brain_age_weights -- weights for brain age


wbir_pseudo_vgg_ -- 2d vgg transferred to 3d


qc networks

Funding

office of naval research

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