Summary
The recipes presented in this chapter demonstrated how to build an end-to-end image classification application with TensorFlow and an Arduino-compatible platform.
In the first part, we learned how to connect the OV7670 camera module to the Arduino Nano and acquire images, with a resolution and color format suitable for memory-constrained devices.
Then, we developed a Python script to create images from the pixels transmitted over the serial by the Arduino Nano. This script was then extended to upload the file images to Google Drive, laying the foundation to build the training dataset.
After the dataset preparation, we delved into the model design, where we leveraged transfer learning with TensorFlow to train a model to classify desk objects.
Ultimately, we quantized the trained model to 8-bit using the TensorFlow Lite converter and deployed it to the Arduino Nano. However, the development of the Arduino sketch went beyond mere model deployment. Crucially, we...
