What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours.
This week, I finished drawing the background image of our app for the frontend. It looks like this:
I also began testing our clothing type, color, and usage models with various pictures of pieces from my own closet as well as online secondhand stores like Depop. I chose to source some pictures from Depop as we want to test our model with pieces from all possible classes. However, we don’t own enough clothing from certain categories(orange, blazers, rompers). However, I realized that we would be able to find pieces of clothing from these categories on online secondhand shops where people would take well-lit pictures of their own clothes to sell which would help supplement our testing data. It was during this testing that I realized that our usage and clothing type models struggled to achieve high accuracies in practice. For example, our model classified the below image as beige, casual, and a dress which is correct.
However, clothing type and usage classification were frequently incorrect. Our models classified a tank top as a blazer and pants as a top, while always outputting a casual usage type even for formal clothing. I realized that this was due to an incorrect training data set. I had initially misunderstood what having a “balanced” dataset means and thought it meant that you need to have enough training data for each class past a threshold.
However, I realized that the amount of data present for each class should also be equal. The accuracy of my clothing type and usage models was inflated since some classes had much more data, like tshirts and casual clothing, while the color model was trained on a dataset where each class had a similar amount of data. Since the imbalance of my dataset was severe for the clothing type, with categories like Tshirts having thousands of examples and categories like blazers having just over 100, I had to source more.
I returned to using the DeepFashion dataset. This dataset was initially insufficient for us because it didn’t label images with all the attributes appropriate(most images were missing color labels). However, it does have many labeled clothing images which works for our application since we are only classifying by color. The updated training and processing code is on our Github. I trained the ResNet50, which reached 50% accuracy. I am also training the ResNet101, which has more layers/parameters, to try and achieve a higher accuracy. If the accuracy of ResNet101 is still insufficient, I will pivot to a different architecture like EfficientNet.
Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the proiect schedule?
My progress is on schedule.
What deliverables do you hope to complete in the next week?
In the next week, I hope to retrain the clothing type and usage accuracies to be more accurate. I also hope to continue helping Gabriella with the frontend and helping Riley integrate the trained models onto the Jetson.
