Clothes Classification

The beauty of Data Science is the flexibility in its application. It can be applied almost to every field: sport, medicine, finance, and fashion too. This project was entrusted to me with the purpose to develop a program able to recognize clothes. Working with images is always fun because it allows you to differentiate from the usual projects.

I received a small dataset containing images belonging to nine clothing categories so I was facing a multiclass classification problem. I decided to use Transfer Learning to solve the problem by making use of ResNet a pre-trained neural network fit for images. Since the number of images per class wasn’t great, I decided to apply Data Augmentation to expand the training input for the model. Thanks to some regularization techniques such as dropout I avoided overfitting which is a common problem to face. Since the class distribution was unbalanced, I evaluated the model with recall, precision, and their harmonic mean, F1-score.

This problem task compared to the others was quite easy and quick. I faced similar problems in my university years, so I was able to manage images and Convolutional Neural Networks. Unfortunately, I can’t post this project's code or additional information because of customer policy.