  To classify images based on their content is one of the most studied topics
in the field of computer vision. Nowadays, this problem can be addressed using
modern techniques such as Convolutional Neural Networks (CNN), but over the
years different classical methods have been developed. In this report, we
implement an image classifier using both classic computer vision and deep
learning techniques. Specifically, we study the performance of a Bag of Visual
Words classifier using Support Vector Machines, a Multilayer Perceptron, an
existing architecture named InceptionV3 and our own CNN, TinyNet, designed from
scratch. We evaluate each of the cases in terms of accuracy and loss, and we
obtain results that vary between 0.6 and 0.96 depending on the model and
configuration used.
