<p>Your memory and processing requirements will be proportional to the pixel size of your image. Whether this is too large for you to process efficiently will depend on your hardware constraints and the time you have available.</p>

<p>With regards to resizing the images there is no one answer, you have to consider how to best preserve information that'll be required for your algorithm to learn from your data while removing information that won't be useful. Reducing the size of your input images won't necessarily be a negative for accuracy. Consider two cases:</p>

<p><strong>Handwritten digits</strong></p>

<p>Here the images could be reduced considerably in size and maintain all the structural information necessary to be correctly identified. Have a look at the <a href="http://yann.lecun.com/exdb/mnist/" rel="nofollow noreferrer">MNIST data set</a>, these images are distributed at 28 x 28 resolution and identifiable to <a href="https://arxiv.org/abs/1202.2745" rel="nofollow noreferrer">99.7%+ accuracy</a>.</p>

<p><strong>Identifying Tree Species</strong></p>

<p>Imagine a set of images of trees where individual leaves could help identify species. Here you might find that reducing the image size reduces small scale detail on leaf shape in a way that's detrimental to the model, but you might find that you get a similar result with a tight crop (which preserves individual leaves) rather than an image resize. If this is the case you may find that creating multiple crops from the same image gives you an augmented data set for training that considerably improves results (which is something to consider, if possible, given your training set is very small)</p>

<p>Deep learning models are achieving results around human level in many image classification tasks: if you struggle to identify your own images then it's less likely you'll train an algorithm to. This is often a useful starting point when considering the level of scaling that might be appropriate.</p>
TensorFlow for image recognition, size of images
<p>How can size of an image effect training the model for this task?</p>

<p>My current training set holds images that are <code>2880 X 1800</code>, but I am worried this may be too large to train. In total my sample size will be about 200-500 images. </p>

<p>Would this just mean that I need more resources (GPU,RAM, Distribution) when training my model?</p>

<p>If this is too large, how should I go about resizing? -- I want to mimic real-world photo resolutions as best as possible for better accuracy.</p>

<p><strong>Edit:</strong></p>

<p>I would also be using <code>TFRecord</code> format for the image files</p>