Getting Started

[中文]

Hardware Requirements

An ESP32-S3 or ESP32-P4 development board. Recommended: ESP32-S3-EYE or ESP32-P4-Function-EV-Board
PC (Linux)

Note

Some boards currently use Type C connectors. Make sure you use the right cable to connect the board!

Software Requirements

ESP-IDF

ESP-DL runs based on ESP-IDF. For detailed instructions on how to get ESP-IDF, see the ESP-IDF Programming Guide.

Note

Please use release/v5.3 or higher version of ESP-IDF.

ESP-PPQ

ESP-PPQ is a quantization tool based on ppq. ESP-PPQ adds Espressif’s customized quantizer and exporter based on PPQ, which makes it convenient for users to select quantization rules that match ESP-DL according to different chip selections, and export them to standard model files that can be directly loaded by ESP-DL. ESP-PPQ is compatible with all PPQ APIs and quantization scripts. For more details, please refer to PPQ documents and videos. If you want to quantize your own model, please install esp-ppq with the following command:

pip uninstall ppq
pip install git+https://github.com/espressif/esp-ppq.git

Quick Start

ESP-DL provides some out-of-the-box examples

Example Compile & Flash

idf.py set-target [Soc]
idf.py flash monitor -p [PORT]

Replace [Soc] with the specific chip, currently supports esp32s3 and esp32p4.

Example Configuration

idf.py menuconfig

Some examples contain configurable options that can be configured using idf.py menuconfig after specifying the chip using idf.py set-target.

Trouble shooting

Check ESP-IDF doc

See ESP-IDF DOC

Erase FLASH & Clear Example

idf.py erase-flash -p [PORT]

Delete build/, sdkconfig, dependencies.lock, managed_components/ and try again.

Model Quantization

First, please refer to operator_support_state.md to ensure that the operators in your model are supported.

ESP-DL must use the proprietary format .espdl for model deployment. Deep learning models need to be quantized and converted to the format before they can be used. ESP-PPQ provides two interfaces, espdl_quantize_onnx and espdl_quantize_torch, to support ONNX models and PyTorch models to be exported as .espdl models. Other deep learning frameworks, such as TensorfFlow, PaddlePaddle, etc., need to convert the model to ONNX first. So make sure your model can be converted to ONNX model. For more details, please refer to:

Model deployment

ESP-DL provides a series of APIs to quickly load and run models. For more details, see: