We provide an example to train a Conv model with Pytorch and convert to Tensorflow Lite (Micro) and then deploy it on ESP32-S3-DevKitC-1-N8R8.

You can play the notebook in Google Colab.

There is a gap between deep models and resource-limited devices. From Kb-level microcontroller to powerful Edge devices with hardware accelerator, the on-board resource varies. It requires model compression and lightweight model designs.

We take the deployment of a simple CNN model on ESP32 as an example. We implement a tiny CNN model with Pytorch and train it on MNIST for epochs, and then convert it to ONNX and then take ONNX-Tensorflow to perform conversion and full-integer quantization. The well-quantized model is exported to a .CC model file.

To deploy TFlite model to ESP32, we implement an inference program for ESP32-S3 and flash it into board with ESP-IDF.

(We will release this part soon)