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espressif/esp-dl: Espressif deep-learning library for AIoT applications

ESP-DL is a lightweight and efficient neural network inference framework designed specifically for ESP series chips. With ESP-DL, you can easily and quickly develop AI applications using Espressif's System on Chips (SoCs).

ESP-DL offers APIs to load, debug, and run AI models. The framework is easy to use and can be seamlessly integrated with other Espressif SDKs. ESP-PPQ serves as the quantization tool for ESP-DL, capable of quantizing models from ONNX, Pytorch, and TensorFlow, and exporting them into the ESP-DL standard model format.

• ESP-DL Standard Model Format: This format is similar to ONNX but uses FlatBuffers instead of Protobuf, making it more lightweight and supporting zero-copy deserialization, with a file extension of .espdl.
• Efficient Operator Implementation: ESP-DL efficiently implements common AI operators such as Conv, Gemm, Add, and Mul. The list of supported operators.
• Static Memory Planner: The memory planner automatically allocates different layers to the optimal memory location based on the user-specified internal RAM size, ensuring efficient overall running speed while minimizing memory usage.
• Dual Core Scheduling: Automatic dual-core scheduling allows computationally intensive operators to fully utilize the dual-core computing power. Currently, Conv2D and DepthwiseConv2D support dual-core scheduling.
• 8bit LUT Activation: All activation functions except for ReLU and PReLU are implemented using an 8-bit LUT (Look Up Table) method in ESP-DL to accelerate inference. You can use any activation function, and their computational complexity remains the same.

• [2025/07/22] Rename the ESP-PPQ package from ppq to esp-ppq to avoid conflicts with ppq, and upgrade all ESP-PPQ dependencies to the latest versions. Note: In the quantization script, pay attention to modifying the package name.
• [2025/04/30] We released a new esp-detection project and the ESPDet-Pico model, which can easily train and deploy object detection models. espdet_pico_224_224_cat espdl model and example is a cat detection model trained by esp-detection. Feel free to try it and share your feedback!
• [2025/02/18] We supported yolo11n espdl model and example.
• [2025/01/09] We updated the schema of espdl model and released ESP-DL v3.1.0. Note: previous models can be load by new schema, but new model is not compatible with previous version.
• [2024/12/20] We released ESP-DL v3.0.0.

• ESP-IDF

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

Please use ESP-IDF release/v5.3 or above.

• ESP-PPQ

ESP-PPQ is a quantization tool based on ppq. If you want to quantize your own model, you can install esp-ppq using the following method. For more installation methods, please refer to the Getting Started.

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
   pip install esp-ppq

Note

• The example code installs the Linux PyTorch CPU version. Please install the appropriate PyTorch version based on your actual needs.

First, please refer to the ESP-DL Operator Support State to ensure that the operators in your model are already supported.

ESP-PPQ can directly read ONNX models for quantization. Pytorch and TensorFlow need to be converted to ONNX models first, so make sure your model can be converted to ONNX models. For more details about quantization, please refer to

how to quantize model
how to quantize MobileNetV2
how to quantize YOLO11n

ESP-DL provides a series of API to quickly load and run models. A typical example is as follows:

how to load test profile model
how to run model

Pedestrian Detection
Human Face Detection
Human Face Recognition
Imagenet Classification (MobileNetV2)
COCO Detection (YOLO11n)
CAT Detection (​​ESPDet-Pico)
Pose Estimation (YOLO11n-Pose)

If you encounter unsupported operators, please point them out in the issues, and we will support them as soon as possible. Contributions to this ESP-DL are also welcomed, please refer to Creating a New Module (Operator) for more details.

ESP-DL Operator Support State

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