This is the implementation of an assignment from the Master of Science's course "Artificial Neural Networks and Deep Learning" of Politecnico di Milano. The project was done in a group of 3, in collaboration with my colleagues Davide Mantegazza and Gabriele Bozzetto.
In this homework we were required to design and train a model to classify images of leafs, which are divided into categories according to the species of the plant to which they belong. Being a classification problem, given an image, the goal is to predict the correct class label. A sample of each class is shown in the image below.
We implemented various CNNs and explored aspects such as regularization, dropout, data augmentation, bias-variance tradeoff, transfer learning, and fine tuning. Our best model exceeded 90% accuracy on the test set.
Each notebook contains a particular model, with images of the architecture and its performance on the validation set. Further details are found in the "AN2DL HW01 report.pdf" file.
