license: apache-2.0
pipeline_tag: object-detection
base_model: yolo26n
tags:
- arm
- arm-optimized
- edge-linux
- raspberry-pi-5
- xnnpack
Arm Runtime for YOLO26 on Raspberry Pi 5
About
This runtime runs YOLO26n on Raspberry Pi 5 with Raspberry Pi OS Trixie. It is a fixed-graph implementation that uses model-specific memory planning, custom operators, and operator fusion to maximize throughput on the Raspberry Pi CPU.
The runtime supports the standard YOLO26n model and compatible fine-tuned variants at square resolutions of 320, 480, 640, 800, 960, and 1280 pixels.
Performance
The following YOLO26n results were measured on Raspberry Pi 5 at 640x640,
batch 1, against the 128-image, 1,025-instance
coco-128-by-sa
published dataset revision. Inference time excludes
preprocessing and postprocessing.
| Runtime | Inference (ms/image) | mAP50-95 |
|---|---|---|
| Arm Runtime (this project) | 36.60 | 0.4863 |
| NCNN | 67.20 | 0.4902 |
| MNN | 90.90 | 0.4753 |
| OpenVINO | 102.00 | 0.4812 |
| ONNX Runtime | 126.90 | 0.4812 |
| ExecuTorch | 136.90 | 0.4880 |
See the performance baseline for the Arm Runtime measurement and host context.
Quick start
Prerequisites
On Raspberry Pi OS Trixie, install the required packages:
sudo apt update
sudo apt install --yes --no-install-recommends \
ca-certificates \
curl \
git \
libgl1 \
libglib2.0-0t64 \
make \
patch \
python3 \
python3-venv \
tar \
gzip
Clone the repository:
git clone https://github.com/arm/Optimized-Runtimes.git
cd Optimized-Runtimes/yolo26-raspberry-pi-5
Run an example
Run object detection on a sample video using the standard YOLO26n checkpoint:
make example-video
For a fine-tuned checkpoint, your own video, or live camera input, see Usage.
Run the benchmark
Run the Ultralytics benchmark harness using this runtime:
make ultralytics-benchmark
See the Ultralytics benchmark documentation for configuration options.
Usage
Acquiring a runtime
A prebuilt runtime library is available from this repository's releases. The
example and benchmark targets download the release matching the checked-out
project version when no runtime exists under dist/runtime/.
To build the runtime from source, install the native build prerequisites:
sudo apt update
sudo apt install --yes --no-install-recommends \
build-essential \
ca-certificates \
cmake \
curl \
git \
patch \
python3 \
python3-venv \
tar \
gzip
Then run:
make runtime
The resulting runtime is installed under dist/runtime/, where the examples
and benchmark consume it automatically.
Acquiring a model
The examples and benchmark require a YOLO26n checkpoint converted for this runtime. If the default model is absent, their Make targets download the pinned standard YOLO26n checkpoint and convert it automatically.
The benchmark normally uses the standard model because its classes match the default COCO dataset. To use a compatible fine-tuned checkpoint with the video or camera examples, convert it first:
make model CHECKPOINT=/path/to/your-checkpoint.pt
The default conversion uses 640x640 input and writes the model under
dist/models/. Select another supported resolution with, for example:
make model CHECKPOINT=/path/to/your-checkpoint.pt RESOLUTION=320
Examples and benchmarks
The examples use the Python API. Their Make targets prepare missing default runtime and model artifacts automatically.
- Object detection on prerecorded video
- Object detection on live video from a Raspberry Pi Camera
- Ultralytics benchmark
Other information
Run make help for other useful commands.
Project layout
src/converter/- Converts compatible YOLO26n PyTorch checkpoints into the runtime model format.src/runtime/- Implements the fixed YOLO26n runtime.src/runtime/patches/- Contains project changes to XNNPACK and pthreadpool.src/runtime/api/python/arm_yolo26n/- Provides the Python API.examples/- Contains prerecorded-video and live-camera examples.benchmarks/ultralytics/- Integrates the runtime with the Ultralytics benchmark.