Model Conversion

1. Raspberry Pi 5 YOLO26 (Benchmark Testing)

The Ultralytics team conducted benchmark testing on YOLO26 across ten different model formats, measuring speed and accuracy. These formats include: PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF GraphDef, TF Lite, MNN, NCNN, ExecuTorch. The benchmark testing was run on Raspberry Pi 5 with FP32 precision, with a default input image size of 640.

Comparison Chart

2. Model Conversion

The first time using YOLO26's export mode will automatically install some dependencies, just wait for automatic completion!

2.1. CLI: pt → onnx, pt → ncnn

Convert PyTorch format models to onnx and ncnn

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cd /home/pi/ultralytics26

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yolo export model=yolo26n.pt format=onnx
# yolo export model=yolo26n-seg.pt format=onnx
# yolo export model=yolo26n-pose.pt format=onnx
# yolo export model=yolo26n-cls.pt format=onnx
# yolo export model=yolo26n-obb.pt format=onnx
​
yolo export model=yolo26n.pt format=ncnn
# yolo export model=yolo26n-seg.pt format=ncnn
# yolo export model=yolo26n-pose.pt format=ncnn
# yolo export model=yolo26n-cls.pt format=ncnn
# yolo export model=yolo26n-obb.pt format=ncnn

2.2. Python: pt → onnx → ncnn

Convert PyTorch format models to TensorRT: conversion process will automatically generate ONNX models

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cd /home/sunrise/ultralytics26/yahboom_demo

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python3 model_pt_onnx_ncnn.py

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from ultralytics import YOLO
​
# Load a YOLO PyTorch model
model = YOLO("/home/sunrise/ultralytics26/yolo26n.pt")
# model = YOLO("/home/sunrise/ultralytics26/yolo26n-seg.pt")
# model = YOLO("/home/sunrise/ultralytics26/yolo26n-pose.pt")
# model = YOLO("/home/sunrise/ultralytics26/yolo26n-cls.pt")
# model = YOLO("/home/sunrise/ultralytics26/yolo26n-obb.pt")
​
# Export model to ONNX format
model.export(format="onnx")  # This will create 'yolo11n.onnx' in the same directory
​
# Export model to NCNN format
model.export(format="ncnn")  # creates 'yolo11n_ncnn_model'
​

Note: The converted model files are located in the converted model file location

4. Model Prediction

CLI Usage

CLI currently only supports calling USB cameras. CSI camera users can directly modify the previous Python code for calling onnx and ncnn models!

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cd /home/sunrise/ultralytics26

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yolo predict model=yolo26n.onnx source=0 save=False show

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yolo predict model=yolo26n_ncnn_model source=0 save=False show

References

Quick Start Guide: Raspberry Pi with Ultralytics YOLO26