This week I completed training a new model using the new dataset which gave me an accuracy of up to 81.6%. From our validation and training steps, it is evident that the model will perform significantly better with additional training. Therefore, I set up Google Cloud to train the model for 150 epochs. Each epoch takes about 15-20 minutes to train and validate. I hope this new training will help us achieve our accuracy rate of 95%. I have also used the confidence level outputted by my model when detecting objects to implement any object prioritization algorithm. In addition, I faced a few challenges this week with the Jetson Nano. The Jetson Nano has suddenly started to be stuck on boot up and not proceed to its environment. Since the model has reached the end of life, there is very little help on this issue. We have temporarily switched to the Jetson Tx2 as there is more help for it, but we plan to try again with a different Jetson Nano concurrently. We prefer the Jetson Nano as its size works well for our product.
My progress is slightly behind schedule as a result of the Jetson issues, but I hope to get back on schedule soon.
Next week, I hope to finish training our final model and incorporate the model into our Jetson. I also hope to have a working Jetson Nano by the end of next week but will continue to use the TX2 as our backup if needed. In addition, I want to test the communications between the Raspberry Pi and the Jetson as well as the communication between the Jetson and the iOS App.
Verification and Validations:
The Verification tests I have completed so far are a part of my model. There are two main tests that I am running. The validation tests and the accuracy tests. The validation tests are a part of the model training. As the model trains, I test the accuracy of the model on images that the model does not see during training. This helps me track not only if my model is training well, but also t ensure that my model isn’t overfitting to the training dataset. Then, I ran accuracy tests on my trained model. This is to measure how good the model is on data that isn’t part of training or validation.
This upcoming week, I plan to run two different tests on my system. The connectivity tests and the longevity tests. I want to ensure that there is proper connectivity between the Jetson and the Raspberry Pi as well as the Jetson and the IOS App. The connectivity between the jetson and the Raspberry Pi is via the GPIO pins. Therefore, testing the connectivity should be straightforward. The connectivity between the Jetson and the iOS App is via Bluetooth. Therefore the connectivity tests will include how far apart can the phone be from the Jetson to ensure proper connection, as well as power requirements to maintain a good Bluetooth connection.
In addition, I will run longevity tests on the Jetson. Currently, our plan assumes that the Jetson will need its own battery to be able to last 4 hours long. However, I want to first check how long the PiSugar module will be able to consistently provide good power for both the Raspberry Pi and the Jetson. Based on the results of that test, I would decide on the appropriate Battery for our Jetson. This test will also depend on if we can get the Jetson Nano working again,