Transfer learning without imresize or imageDatastore

2 次查看(过去 30 天)
For many samples of small images, it would be nice to load the data into RAM and perform transfer learning without using images on slower memory (imageDatastore). Unfortunately, if I had 50x50x1 images and had to resize to go with alexnet, I will be forced to use (50x50x1/227x227x3) ~ 1.6% of the number of samples in order to keep everything in RAM.
Does anyone know a fix? A custom layer that resizes would work, that's a lot of work though.
using 2017b

回答(1 个)

Shounak Mitra
Shounak Mitra 2018-8-20
Hi Michael,
Thanks for your question.
If you need to resize, apply augmentedimagedatastore to your image datastore - it will be much faster than a custom readfcn, because it preserves prefetch under the hood. If you don’t need to resize, augment, or any other need for a custom readfcn, then vanilla imds is simplest.
Another option is to create a custom layer but you're right, it'll take some work.
HTH Shounak
  1 个评论
Michael Benton
Michael Benton 2018-9-2
编辑:Michael Benton 2018-9-4
https://www.mathworks.com/help/nnet/ref/augmentedimagedatastore.html according to this, that can be done. I have updated to 2018a, and this works, thanks

请先登录,再进行评论。

类别

Help CenterFile Exchange 中查找有关 Image Data Workflows 的更多信息

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by