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.
I ended up being unable to use the EfficientNet architecture because of a bug which I discussed in our weekly meeting this week. However, I retrained our colour and clothing type models using a balanced dataset and tried our the ResNet50,101, and 152 architecture. The ResNet152 performed best for clothing type classification and the ResNet101 performed the best for color classification, each reaching a validation accuracy of 65-75%. I also manually compiled a small dataset of 80 images(including at least 5 examples from each type and color class) which I used as a sanity check to ensure that our models performed well on the types of images that Style Sprout users would take. This spreadsheet includes the accuracy for the ResNet50 and ResNet152 versions of the models, and I will update it to include the accuracy for the ResNet101 after training is finished.
I also did some troubleshooting of our usage model since it was only outputted the “sports” usage(which has now been switched to casual). My troubleshooting, which included running the 3 tests discussed in our weekly meeting, and I have determined that my next course of action is to try and determine the usage for less categories. This means determining only if clothing types like tops and skirts are formal or casual and ignoring clothing types like blazers and jeans, which are inherently either casual or formal.
I also worked with Gabriella in person on Monday to test the uses until dirty feature. We had to make a couple of changes to our backend and frontend, which are located on our Github, but were able to successfully complete this feature. I also added the ability to display a variable # amount of pictures when an outfit is generated. Before, it only displayed a top and bottom, however now there is the ability to show outfits with 3 pieces of clothing(jacket, top, bottom), 2 pieces of clothing(jacket and dress, top and bottom), or 1 piece of clothing(dress). This is located on this branch on our Github.
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 finish the usage model and continue helping Gabriella with the backend/frontend.
Verification Tests:
I tested the accuracy of our models in two ways. Our dataset was split into two parts, training and validation(training is 80% of the original, validation is the remaining 20%). When training each model, the validation dataset was used to measure the performance of the model. For the clothing type and color classification models, the validation accuracy ranged from 60-70% depending on the architecture that was used. Since each model is a 1-of-k classifier, where each image belongs to one of k classes, the validation accuracy is categorical accuracy, meaning # of correctly classified images/total correctly classified images. A correctly classified image is an image where the predicted class is the same as the actual class.
I also tested the accuracy of our models using a small dataset of 80 images containing 5 examples of each of our 16 possible clothing types and about 6-10 images for each of our 12 colors. I manually compiled this dataset. I evaluated the predictions made by our models using categorical accuracy and a spreadsheet linked earlier in my status report. I haven’t finished checking the accuracy for the ResNet101 clothing type and color models since they are still training.
However, it is highly likely that none of our models will meet the 80% accuracy use case requirement we had for classification accuracy. We are supplementing this with the additional feature of having the user confirm/correct the labels produced for each piece of our user’s clothing, which should correct any incorrect labels to achieve a 100% accuracy for clothing classification in our database.
