![]() The first of these is training a small model from scratch on what little data you have (which achieves an accuracy of 82%). In Chapter 5 of the Deep Learning with R book we review three techniques for tackling this problem. We’ll use 2,000 pictures for training – 1,000 for validation, and 1,000 for testing. As a practical example, we’ll focus on classifying images as dogs or cats, in a dataset containing 4,000 pictures of cats and dogs (2,000 cats, 2,000 dogs). A “few” samples can mean anywhere from a few hundred to a few tens of thousands of images. ![]() Having to train an image-classification model using very little data is a common situation, which you’ll likely encounter in practice if you ever do computer vision in a professional context.
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