This week I began learning how to use Tensorflow to build a Convolutional Neural Network and train a machine learning model. I did a tutorial online and found that it was surprisingly easy and intuitive.
Originally, the convolutional neural network was supposed to classify between 6 different types of objects: Cat, Dog, Squirrel, Raccoon, Legs, and Shoes. After taking sample images using a camera that was set up at the cat door height from one meter away, I realized that almost all of the time, the camera would see a human’s entire lower body, from the hip to the feet. Therefore, instead of classifying Legs and Shoes, we will get rid of both of them and classify Lower Body. I had previously found a large image data set of humans which contained annotated human body parts, so I wrote a python script to parse through some 10 gigabytes of images and only keep the ones which contained a human’s lower body. Then, I properly arranged each set of images into a directory for the Tensforflow script to read.
Now that the Tensorflow script has been written, the convolutional neural network built, and the data set prepared, I will test on a small sub-data set to ensure that the script is correct and then deploy the entire thing to AWS. I will slowly get to these two tasks over Spring Break and have them done before school starts again.