Moving into the final stretch of the semester, the most significant risk to our project is the accuracy of the image classifier and how that may affect the overall classifier. We were able to get pretty high confidence levels for most of our materials. However, our image classifier does not seem to be able to classify paper as recyclable with any confidence. To mitigate this risk, we have decided to use different weights for our separate accuracy rates per material based on how frequently certain items are thrown away to calculate our overall classifier accuracy (more specifically, we’re weighting each material by % of MSW generated, which is the proportion of each material out of all waste produced). This will allow our overall system accuracy to be a more accurate reflection of what it would be in the real world.
The most significant change we made this week was implementing the weighting system that I just described for each material’s accuracy weight. We feel that doing this will provide us with a more accurate overall accuracy rate for the entire system as if it was in full operation on a college campus, compared to if each category was weighted equally. One change that we may make to the design in the next week is retraining our ResNet-101 model to allow HDPE plastics to be considered as recyclable for the image classifier (the pictures of the HDPE plastics are currently in the trash training and validation folders). Retraining is necessary in order to improve our overall accuracy for plastics.
We are currently on track. We are preparing for our Final Presentation next week and are still mainly focused on increasing the overall accuracy of the system, but we do feel confident with our most recent accuracy data which can be found here.
Here are some images of the image classifier working with high confidence levels.
