<p>Higher resolution images will result in a higher training time and an increased memory consumption (mainly GPU memory).</p>

<p>Depending on your concrete task, you might want to reduce the image size in order to therefore fit a reasonable batch size of let's say 32 or 64 on the GPU - for stable learning.</p>

<p>Your accuracy is probably affected more by the size of your training set. So instead of going for image size, you might want to go for 500-1000 sample images. Recent publications like <a href="https://arxiv.org/abs/1512.02325" rel="nofollow noreferrer">SSD - Single Shot MultiBox Detector</a> achieve high accuracy values like an mAP of 72% on the <a href="http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html" rel="nofollow noreferrer">PascalVOC</a> dataset - with "only" using 300x300 image resolution.</p>

<p>Resizing and augmentation: SSD for instance just scales every input image down to 300x300, independent of the aspect ratio - does not seem to hurt. You could also augment your data by mirroring, translating, ... etc (but I assume there are built-in methods in Tensorflow for that).</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>