My first milestone was getting the object detection code up and running on the Raspberry Pi. I first put the microchip into the SD card, which was inserted into my computer. Then, I downloaded the Imager.exe file that contained the operating system for the RaspberryPi. That same microchip was placed into the Raspberry Pi. Next, I placed the mouse receiver into one of the USB ports in the Raspberry Pi. I proceeded to connect the Raspberry Pi to my TV with the HDMI adapter, the power cord to an outlet, and the PiCamera to the Raspberry Pi. The Raspberry Pi popped up on the screen, allowing me to configure the settings to my preference. When I had finally finished setting up the Raspberry Pi, I got to test out the PiCamera to see if it is working. After my Raspberry Pi was ready, I decided to do some research about object detection. I used NanoNets at first to understand some of the general concepts about how object detection works and such, but I found out that NanoNets is a bit outdated to serve my purposes. I later focused on a tutorial I found on github that walked me through a tutorial on how to get object detection code on the Raspberry Pi. I did run into a couple problems while attempting to download OpenCV, but everything else went smoothly. This code required me to download both TensorFlow and OpenCV, as well as a ready-to-use model that was already trained for me. When I finally finished the tutorial, moving the PiCamera around made the screen show the respective boxes and identify everyday objects with ease.