Give it a try with data augmentation. (And don't forget to split you dataset into a train set and a test set).

For example flip and rotate will multiply your number of images by 12.

Create a black and white version will multiply by an extra factor of 2.

And then you can go with random crops, random rotations, random colour modifications, random shear, random scaling.

This will give you a potentially infinite amount of image variation.

You can also use dropout as part of your network to avoid overfiting.

And on top of that, remember that when working with convolutional neural networks, an image is not a single datapoint. Each pixel (and it's attached neighbourhood) is a datapoint, so you potentially have thousands of training sample per image depending on the receptive field of your CNN.

One thing to be careful about when designing you data augmentation pipeline is to make sure the chip / crack is visible after the cropping, so make sure to visually check what you feed into your network.