For this week, I continued with data collection, expanding our datasets by importing annotated images which I have found online. I have also performed further fine-tuning of our training hyperparameters, testing different augmentations and hyperparameter optimizations in order to improve our detection accuracy, incorporating the expanded dataset in order to improve the training process. I then ran a preliminary test for our YOLOv5x model to test its detection accuracy. As expected, the YOLOv5x model performs at a higher accuracy, though it results in a longer inference time when run locally.<\/p>\n
In term of progress, I am on track by improving model accuracy while also maintaining real-time performance.\u00a0 The next step in our progress chart is integration, and I will begin work with integrating our model with the Raspberry Pi and the model app.<\/p>\n
For next week, I aim to run inference on cloud and measure inference timing in order to quantitatively assess the latency of our model. I will also continue to work on training and enhancing our YOLOv5x model, and conduct more real-world tests to measure the performance of our optimized model.<\/p>\n","protected":false},"excerpt":{"rendered":"
For this week, I continued with data collection, expanding our datasets by importing annotated images which I have found online. I have also performed further fine-tuning of our training hyperparameters, testing different augmentations and hyperparameter optimizations in order to improve our detection accuracy, incorporating the expanded dataset in order to improve the training process. I … <\/p>\n