The purpose of this project is to create a Machine Learning model
which will be able to classify
different kind of images in two groups, glasss
and no glasses
.
In this model it would be applied a pretrained model MobileNet V2
, developed by google, and it will be applied fine-tuning
with the weight of the pretrained model.
The model is based on Tensorflow/Learn/Tutorials/Images/Transfer learning and fine-tuning
- https://www.tensorflow.org/tutorials¶
📍 Programming language: Python
📍 Library: Tensorflow
📍 Applied algorithm: Convolutional Neural Network
- Main source: https://www.kaggle.com/jeffheaton/glasses-or-no-glasses
- Secondary source: 1 Million Fake Faces - 1 - https://www.kaggle.com/tunguz/1-million-fake-faces, from which I took 311 adittional photos belonging to the class no_glasses
- Some images from the train sample:
- Example of data augmentation:
- Application of fine tuning (after the green line):