I am a beginner to Deep Learning and have read some tutorials. Now I want to try something like LeNet on my own data, but I do not know how I should prepare it as a suitable training input for LeNet. Currently, all of the images in my dataset have been stored in a folder and I have an excel file that contains the information about the label of each image. I am confused in what format/data type I should store all the N
images and the output (label) vector?
I know that the output should be a [Nx1]
vector of the labels of images, but not sure if I should have a similar [NxP]
matrix for the input images where each row of the vector represents an image ( width: w, height:h; P=wxh
).
Furthermore, do images have to have the same size?
Thank you in advance.
Best Answer
In case you're more concerned about having a model than learning the intricacies of deep learning a good idea could be to follow this tensorflow tutorial, using python as your tag mentions. Between installing tensorflow and following the tutorial it will probably take you around 2-3 hours, you will not have to code anything.
Under the hood this does the following:
You end up having a very powerful model even if you have very few images (you probably need at least 100). It also handles the different resolutions for you.