This week I mainly focused on integrating the whole subsystem and with the other subsystems.
The cv part now produces results like this which can distinguish all different pieces. 
The subsystem now outputs a request to the LED system for the leds to turn on and off when capturing a frame, and a FEN string to the integrate server as a result of board state detection.
I’ve also discovered some small changes that need to be made to the design of the physical board. like the need to raise the leds as close to the board as possible for the light to be seen clearly by the user.
Next week I will continue to address those small changes to improve user experience.
About new knowledge for this project, this is my first time learning about computer vision, I learned not only how to use the code for YOLO models, but also how different things would affect the outcomes of the models, how I should take the photos for the datasets to address different edge cases, how different yolo models work. About learning strategies I learned a lot by reading the official documents of yolo and anylabeling(the tool I used to label datasets), blogs about similar chess board state detection projects, and I also got help from Chatgpt when YOLOX didn’t support mps on macOS much and I had to learn to modify the code for yolox to support mps.