Deep Learning: How do I add regression ground truth data to an imageDatastore?
11 views (last 30 days)
Show older comments
Tomas on 13 Dec 2017
Answered: Daniel Cohen on 13 Jan 2021
if I train my network with newnet=trainNetwork(imageDatastore_traindata,net,options) imageDatastore_traindata.Labels need to be categorical, i.e. not regression but classification labels. So for regression, it seems I need to use newnet=trainNetwork(X,Y,net,options)
and give my regression labels as Y, but then I can't use the imageDatastore as X, so I'll run into memory problems. Is there no way to do regression with an imageDatastore?
Bashar Saad on 28 Dec 2018
hi friend i have same the problem i do not know how to load my own dataset in deep learning could you help me plase ?
Amy on 19 Dec 2017
You can use an image datastore to get the image file names (without having to load the images from the dataset into memory at all), then create a table with the file names in the first column and your regressors in the other columns, and feed that into trainNetwork.
(see the trainedNet = trainNetwork(tbl,layers,options) syntax for trainNetwork.)
Simone Scaringi on 21 Feb 2020
I've spent way too much time getting my head around this.
What I ended up doing to "fix" it, is to save my data files as .png images. In my case these are just 1D "images", but I guess you can save the 3D matrix as an image as well. If you do this then train proceeds as expected since the table with file paths have .png "images" in them.
Hope this helps to whoever reads this!
Daniel Cohen on 13 Jan 2021
% trainedNet = trainNetwork(ds, layers, options) trains and returns a
% network trainedNet using the datastore ds. For single-input networks,
% the datastore read function must return a two-column table or
% two-column cell array, where the first column specifies the inputs to
% the network and the second column specifies the expected responses. For
% networks with multiple inputs, the datastore read function must return
% a cell array with N+1 columns, where N is the number of inputs. The
% first N columns correspond to the N inputs and the final column
% corresponds to the responses.
What what does that mean for this situation?
Find more on Get Started with Statistics and Machine Learning Toolbox in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!Start Hunting!