![]() ![]() ![]() In my personal opinion CircuitPython is easy and fun to work with. Lastly, everything needs to be done using CircuitPython. So we use a 120x160 TFT LCD display to show the output to the user. ![]() I will discuss this in more detail in a later part of this article.We also want to show our results on an LCD screen. Therefore, creating compact machine learning models that fit in Pi Pico’s RAM and is accurate enough for our work is a major challenge we are aiming to overcome. It is important to note that our machine learning model can be executed entirely on a Raspberry Pi Pico a connected computer or cloud are not required. An image of the project in action is shown below: We intend to run a machine learning model on our Raspberry Pi Pico that analyzes photos received from a camera and tries to infer what digit was present in the image. □Link to project’s repository : Table of Contents Our Goal Required Hardware Required Software Wiring Understanding OV7670’s image format Postprocessing camera images Training ML model Exporting ML model Pico friendly format Guidance on setting up the project Troubleshooting Future Our Goal Even after you follow all the recommended steps in this article, some tinkering will still be necessary to get it to work. In this article, I’ll share how I used a Raspberry Pi Pico, an OV7670 camera module, a 128x160 TFT LCD display and machine learning to build a handwritten digit classification system.
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