CODEWITHSUNDEEP
image-processingmachine-learningtensorflowcomputer-vision
Adam P
Adam P

Reputation: 382

Does image size matter when training with TensorFlow?

I was wondering if there is any benefit to training on high resolution images rather than low resolution. I understand that it will take longer to train on larger images and that the dimensions must be a multiple of 32. My current image set is 1440x1920. Would I be better off resizing to 480x640, or is bigger better?

Upvotes: 6

Views: 10205

Answers (2)

Mark McDonald
Mark McDonald

Reputation: 8190

It's certainly not a requirement that your images be powers of two. There may be some cases where it speeds things up (e.g. GPU allocation) but it's not critical.

Smaller images will train significantly faster, and possibly even converge quicker (all other factors held constant) as you will be able to train on bigger batches (e.g. 100-1000 images in one pass, which you might not be able to do on a single machine with high res imagery).

As to whether to resize, you need to ask yourself if every pixel in that image is critical to your task. Often this is not the case - you can probably resize a photo of a bus down to say 128x128 and still recognize that it's a bus.

Using smaller images can also help your network generalise better, too, as there is less data to overfit.

A technique often used in image classification networks is to perform distortions (e.g. random cropping, scaling & brightness adjustment) on images to (a) convert odd-sized images to a constant size, (b) synthesize more data and (c) encourage the network to generalise.

Upvotes: 10

DomJack
DomJack

Reputation: 4183

This depends largely on the application. As a rule of thumb, I'd ask myself the question: can I complete the task myself on the resized images? If so, I'd downsize to the lowest resolution before it makes the task more difficult for you yourself. If not... you're going to have to be -very- patient using images 1440 * 1920. I imagine you'll almost always be better off experimenting with more varied architectures and hyper-parameter sets on smaller images compared to fewer models on full resolution images.

Whatever size you choose, you'll have to design your network for the image size you have in mind. If you're using convolutional layers, a larger image will require larger strides, filter sizes and/or layers. The number of parameters will stay the same for each convolution, though the number of features will grow (along with batch normalisation parameters if you're using it).

Upvotes: 5

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