YOLO26n-ONNX

Renesas/YOLO26n-ONNX

YOLO26n-ONNX is a object detection model from Renesas. Use it when you need objects located in an image. The card lists the license as apache-2.0.

This repository hosts YOLO26n, targeting the Renesas R-Car X5H platform for object-detection inference on the NPX6 NPU.

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Updated Oct 9, 2026

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9.9 MB

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At a glance

Task: Object Detection
License: apache-2.0
Access: Public
Created: Sep 23, 2026
Updated: Oct 9, 2026
SHA: 7a7d982f

Base models

ultralytics/yolo26n

Model card

From the Hugging Face model README

YOLO26n (ONNX) – Renesas X5H

Introduction

This repository hosts YOLO26n, targeting the Renesas R-Car X5H platform for object-detection inference on the NPX6 NPU.

Model Architecture: YOLO26 (nano variant) — the latest Ultralytics single-stage real-time object detector family, offered here at the smallest ("n") scale for edge deployment
Source Model: ultralytics/yolo26n
Task: object-detection (dataset: coco)

Deployment Flow

The FP32 ONNX model is auto-cast to INT8 by the Renesas MWMX toolchain at compile time — no separate quantization step is required.

yolo26n.onnx (FP32)
        │
        └─▶  MWMX Runtime  ──▶  INT8 auto-cast  ──▶  NPX6 NPU

Provided Artifacts

Artifact	Status	Notes
FP32 (ONNX)	⏳ Pending	`fp32/yolo26n.onnx` — to be added; will be auto-cast to INT8 by the MWMX toolchain at compile time (see Deployment Flow above); no separate INT8 file will be shipped

Performance

Measured on Renesas R-Car X5H via the MWMX runtime (APM80 ship-performance CI pipeline).

Benchmark configuration: Single NPU · Batch size: 1 · Input: 3 × 640 × 640

AI Cores	Runtime	Precision	Device	Latency (ms)	Type
1	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 1 Core · 850 MHz	6.21	Measured
3	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 3 Core · 850 MHz	4.55	Measured
4	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 4 Core · 850 MHz	4.51	Measured
6	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 6 Core · 850 MHz	4.33	Measured
12	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 12 Core · 850 MHz	5.03	Measured

Accuracy

TBD — not yet measured/published for this repo.

ONNX Runtime – Renesas EP v2.1.0 (MWMX 2.2 ENG4)

Measured on R-Car X5H with ONNX Runtime + Renesas Execution Provider (INT8 auto-cast); nodes the NPU cannot run fall back to the CPU EP. Batch size 1, 1 NPU. Source: ORT Renesas EP test report, status 2026-09-30. This section supersedes earlier ORT figures in this card.

Component	AI Cores	Latency (ms)	Throughput (fps)	NPU Inference (ms)	NPU %	CPU + Overhead %	Portable pkg NPU-only (ms)
Full model	1	10.094	99.1	7.106	70.4 %	29.6 %	6.18181
Full model	12	12.633	79.2	5.625	44.5 %	55.5 %	5.00446

12-core latency is not always lower than 1-core: small models are dominated by CPU-side overhead.

Graph partitioning (NPU vs CPU nodes)

Component	Total Nodes	NPU Nodes	CPU Nodes (Q/DQ inserted)	NPU %	CPU %
Full model	301	286	15 (2)	95.0 %	5.0 %

Compile notes (from the test workbook)

Nodes dispatched to CPU for compilation/latency: 1

Runtime Details

MWMX Runtime

Engine: Renesas MWMX (Middleware MX) native inference runtime
Input format: FP32 ONNX (compiled by the MWMX toolchain)
NPU execution precision: INT8 (auto-cast by MWMX toolchain)
Execution target: NPX6-48K NPU on R-Car X5H

Prerequisites

To run inference on Renesas R-Car X5H, you need:

Renesas R-Car X5H board with NPX6 NPU
Renesas MWMX Runtime
Hugging Face CLI to download the model

Download

hf download Renesas/YOLO26n-ONNX --repo-type=model --include "fp32/*"

Benchmark Methodology

HIL runs: Hardware-in-the-loop — measured on physical R-Car X5H silicon via the MWMX runtime (metawaremx_runtime CI pipeline, "APM80" ship-performance target)
Precision: FP32 ONNX input; INT8 execution (auto-cast by MWMX)

Task: Object Detection
License: apache-2.0
Created: Sep 23, 2026
Updated: Oct 9, 2026