In order to get an idea on how to build an image classification program, we searched for online tutorials. From Youtube, we watched several examples on different image classification projects, including training a computer to read handwritten numbers and how to identify between cats and dogs. The tutorials are for supervised machine learning with CNN (convolutional neural networks).

We followed the tutorial on the cats vs. dogs program. We used a dataset of 25000 images from Kaggle and implemented the Python code on Jupyter Notebook. The filenames in the dataset are in the format name.number.png where “name” is the name of the animal”. For example, cat.1.png. This form of labelling is to make the training of the model simpler. From the filename it will take the name of the animal.

From the output on terminal, we were able to see that the loss decreases and the accuracy increases at every iteration. We were able to display the statistics on TensorBoard as a graph.