Is it a bird? Is it a plane?

Well the age old question does not need to worry you any more. ALL NEW AI can now solve this problem for you!

There are two main folders to this project. Data and plane-or-bird. Data contains all the required data for the model to run. Clearly these are png files and so some preparation is needed before they can be analysed. data_clean.py() does this for you!

data_clean.py will import the png files and transform them into numpy arrays or in human language, turn them into files a computer can read (numbers). This file will turn any 32x32 rgb image into a format readable by the models used later.

In future versions there will be accessibility for the inclusion of other images of other sizes, however for now please keep them to 32x32 in rgb format.

DenseNET is the name of a deep learning algorithm that produced unbelievable results with such little training data, in fact the model was originally created to perform well on this specific dataset; however the original dataset contained many more training images and classes, this is a simplified version.

Your own model can be trained by running the dense_net.py file. Otherwise a model can be loaded by following instructions (insert instructions) from the pretrained weights file deepCNN.h5. With these weights an accuracy of nearly 90% can be expected on unseen data on just 10 epochs, (with loss still decreasing and validation accuracy increasing) so these weights could be used as a starting point to an improved model.

With the use of google colab for GPU, I was able train a model on 200 epochs. There are 10000 training images (5000 birds and 5000 planes), and 2000 testing images (1000 of each).

To extend the training data and to also help generalise I applied data augmentation, which took the form of rotating images, zooming in or out, flipping the images and moving horizontally and vertically. This extends the dataset allowing for more training images but also helps generalise as the augmentation is done randomly and so no two images are exactly the same.

With 20 seconds per epoch after 183 epochs an accuracy of 95.1% was achieved on the testing data, after personally looking through a lot of the images More updates soon.