Now that our program is able to classify images to a certain degree of accuracy, we started thinking about our UI. We want to make a UI so that during our presentation, we will not have to run our program through terminal and we will also be able to make it interactive. Our idea is to allow the users of the program to upload an image and have the model classify what it is an image of. So that our data is not completely random, we were thinking that our model should be trained to classify objects that we can find in the classroom. The objects could be chairs, tables, laptops, headphones, water bottles, backpacks, etc.

The struggle we faced was finding a good dataset. The dataset we used for cats and dogs contained a great number of images for training. Finding the dataset for the classroom objects was more difficult. In the end, we were able to find CalTech101 that contains images of a variety of objects from different categories. There are fruits, animals, office supplies, and vehicles. We decided to use only several objects from this dataset: camera, laptop, watch, chair, headphone. However, the number of images still doesn’t compare to the cats and dogs dataset. Some has only around 50 images while others have up to 200.

To accommodate the reduced amount of testing data, we tried increasing the number of training iterations in our program. When we tested it, it was able to classify the images even though the accuracy is not 100%.