In this article we will work with an image dataset to train an Image classifier using a custom CNN built with TensorFlow. PS: For those who don't already know what is Deep learning or CNN this article may be difficult to understand and unfortunately there is no easier way around this. This article is not meant to be a tutorial about Computer Vision or Deep Learning, For those familiar with these concepts please read on. We will work with a dataset provided here. This dataset is a curated nicely, cleaned and arranged collection of roasted coffee beans in train and test folders.

In this post, I am going to explain a end-to-end use case of deep learning image classification in order to automate the process of classifying defective and non-defective castings in foundry. Casting Process: Casting is one of the major manufacturing process in which molten metal is poured in to a cavity called mould and allowed to cool till it gets solidified into product. Casting defects: These are the defects in the cast product occurred during the casting process and they are undesirable.There are many types of defect in casting like blow hole, pin hole, burr, shrinkage defects, mould material defects, pouring metal defects, metallurgical defects etc. Casting defects are undesirable and cause loss to the manufacturer, therefore the quality department have to do visual inspection of the products and separate the defective one from the good castings. The visual inspection is labour intensive and time consuming, therefore Convolution Neural Networks (CNN) could be used to automate this process by image classification. The figure 1. shows the Casting Inspector app developed in this project.