Felix Gomez Guillamon Jurado

Image Classification DL Project

Developed an Image Classification Deep Learning model with TensorFlow, Keras and OpenCV which is able to detect whether a person is happy or sad in any given image.


  • Used TensorFlow metal for ARM-based mac devices.


  • Loaded and cleaned image datasets.


  • Splitted data into training, validation and test size.


  • Used Adam optimisation.


  • Visually displayed the model’s accuracy performance using Matplotlib’s plotting capabilities.


  • Added 3 Convolutional and MaxPooling layers.


  • Saved model in a ‘.h5’ file to enable model usability from outsiders.


  • Created associated ‘.yaml’ file for creating a working conda virtual environment.


  • Created a Basic Flask Application Front-End. Users can upload any picture of a person and output a result of the sentiment within the picture. It also gives an associated probability.


  • Front-End is done with plain HTML.


  • Implemented security features so that cookies sent and received from the browser are encrypted with a secret key.
image classifier notebook
image classifier
flask front end
felix gomez guillamon
felix gomez guillamon