This week, Jessica and I retrained the image classifier model using the ResNet-101 model instead of ResNet-50, since the accuracy before wasn’t very good. I updated the sensor classifier a bit to reduce fluctuation between readings and make it more consistent, and modified the code for the switch (which is used for detecting whether someone closed the lid) to make it work properly (previously, it was detecting whether the lid was opened and whether the lid was closed). I helped test the classifiers with real objects, and updated some of image classifier confidence levels according to the tests. I also worked on coming up with the distribution of weights for different categories we used to calculate the accuracy of the different classifiers (sensor, image, overall) – we decided to use % material by MSW generation, which is the % of a certain material out of all waste generated/produced. For our slides, I calculated the accuracies of the different classifiers and plotted the accuracy per epoch of the ResNet-101 model.
My progress is on track.
Next week, I hope to start recording videos of our project for the video demo.