# Image Set Collection

As you begin the process of collecting images for your Deep Learning project, you will want to log the images by line, by date, by product, etc.

Tip: The more information that you can provide, especially in the image file naming, will streamline the programming of Deep Learning. Once you have a collection of images, you can organize them into Image Sample Sets.

The most important factor for programming Deep Learning will be creating an image set that is based on what you expect the software to encounter during its deployment phase. Your images should contain all the information that will be needed for Deep Learning to reach the correct decision. Look for scenarios where your manual inspectors may pick up parts, then manually tilt and rotate them to examine for defects. This will indicate that you will probably need angled imaging or lighting to capture those defects.

Another possible scenario would be where a human inspector sees dust or oil on a part, they pick it up and manually wipe off the dust/oil. If this dust/oil could be confused with a defect, you will need to teach Deep Learning about the dust/oil.

This image set will need to include the full range of possible variations that can be captured by the camera. The goal for this is to properly generalize the data set.

Generalization refers to the concept in deep learning of determining how effective the tools will be when used on newly acquired images that weren’t used during training. A well generalized tool will perform well on new data. In this scenario, the model formed by the neural net should fit the initial training set, and account for new data it encounters in unseen images.