For this week, I focused on expanding our existing dataset of annotated fridge images. I identified several datasets from online and made use of them to train and update our existing YOLOv5 model. Furthermore, I experimented with data augmentation techniques(i.e rotations\/ occlusions) to improve the robustness of our model. Furthermore, I spent time conducting research on the different YOLOv5 models and their expected accuracy\/latency for our design report, in order to determine which model will be optimized for our use.<\/p>\n
In terms of progress, I am currently on schedule and have completed the development of the training pipeline in PyTorch, and am working on training the model with our datasets.<\/p>\n
For next week, I will explore training with the YOLOv5x model with further hyperparameter tuning, with the aim of increasing our detection accuracy to beyond 90%.\u00a0 I will also compare inference timings with and explore model quantization for Raspberry Pi optimizations, in order to identify the model which best meets our requirements.<\/p>\n","protected":false},"excerpt":{"rendered":"
For this week, I focused on expanding our existing dataset of annotated fridge images. I identified several datasets from online and made use of them to train and update our existing YOLOv5 model. Furthermore, I experimented with data augmentation techniques(i.e rotations\/ occlusions) to improve the robustness of our model. Furthermore, I spent time conducting research … <\/p>\n