YOLOv26n: Optimized QAT & Deployment on ESP32-P4

High-Performance Object Detection on the Edge

Deploying sophisticated object detection models on resource-constrained microcontrollers often requires a delicate balance between accuracy and latency. This project addresses this challenge by providing a specialized implementation of YOLOv26n optimized specifically for the ESP32-P4 SoC. By leveraging Quantization Aware Training (QAT) and hardware-specific optimizations, it achieves a significant performance boost over standard baselines.

The implementation stands out by delivering a 30% speedup compared to official ESP-DL YOLOv11n benchmarks, reaching a latency of approximately 1.7 seconds on the ESP32-P4 hardware. This is achieved through a combination of architectural refinements and the use of the ESP-DL library’s high-performance neural network kernels.

Architectural Innovations

The project utilizes a custom dual-head architecture that enables NMS-Free (Non-Maximum Suppression) prediction. By employing a One-to-One prediction head for direct inference, the system eliminates the computational overhead typically associated with post-processing steps like NMS.

Furthermore, the model uses direct regression with RegMax=1. Unlike architectures that rely on Distribution Focal Loss (DFL) with higher RegMax values, this approach reduces output channel complexity and simplifies the post-processing pipeline, making it ideal for the ESP32-P4’s hardware accelerator.

Quantization Aware Training (QAT) Pipeline

To maintain high accuracy while using Int8 quantization, the project includes a comprehensive Python-based QAT workflow. This pipeline, orchestrated via a Jupyter Notebook, handles the entire transition from a PyTorch model to a deployable .espdl artifact. Key features of the quantization process include:

• Custom Export Patches: Modifies Attention modules to use static reshaping, ensuring compatibility with the ESP-DL static graph compiler.
• Sensitive Layer Analysis: Automatically identifies and disables quantization for auxiliary branches to stabilize training.
• PPQ Integration: Uses the esp-ppq library for graph simplification, fusion, and calibration using methods like KL divergence.
• Custom Validation: Includes a validator that simulates quantized graph execution to report realistic on-target mAP metrics during the training process.

Firmware Implementation and Deployment

The firmware component is built using ESP-IDF v5.5+ and is written in C++. It features a dedicated inference engine, Yolo26Processor, which is optimized for ESP-DL’s static graph execution. The firmware supports dynamic resolution switching, with pre-configured support for both 512x512 and 640x640 resolutions.

Deploying the model involves copying the generated .espdl artifacts into the ESP-IDF project structure. The firmware is designed to run on the ESP32-P4 Engineering Sample, taking full advantage of the SoC’s SIMD kernels and hardware acceleration capabilities. This project serves as a robust template for developers looking to implement state-of-the-art computer vision on Espressif’s latest high-performance silicon.