This week I worked on fine tuning the model. I found a dataset that had fire from various point of views and perspectives. My main concerns during this process were: generalization of the perspective (can the model find our use case), quantity of sata (can this actually fine tune), and generalization to picam quality.
I fine tuned 11n, 11s, 11m. 11n was the smallest and took half an hour to fine tune 50 epochs. The results were promising. I also took some test images of fire in pans (our use case) on my phone and it generated a clean bounding box. 11s and 11m gave pretty bad results in comparison. Much noisier bounding boxes and under detection. We decided to use 11n going forward.
Once the picam arrived I did some testing using that image quality. However, it performed really well and detected 95% of our testing images. I wrote a script that took images every 2 seconds and then inferred on them.
One limitation of the current model is it’s night time performance. For the sake of MVP, this is not a pressing concern but is something I want to focus on after MVP. This will likely involve me making custom data.
I also helped out on the circuit testing. On that front, we found how to get a clear wave to drive the speaker with. We saw promising results on candle testing but need the collimater to get better results.
Next week I plan to work on the controls loop and interfacing since we didn’t have all the necessary hardware before break. Then I will integrate this with the vision code for a rudimentary loop.