Renesas/Swin-Base-ONNX

Swin-Base (ONNX) – Renesas X5H

A smaller Swin-Tiny-ONNX repository already exists in this catalog. This Base-size checkpoint is kept as its own separate repo (rather than an artifact variant under Swin-Tiny-ONNX) for consistency with that precedent.

Introduction

This repository hosts Swin Transformer (Base) targeting the Renesas R-Car X5H platform for image classification inference on the NPX6 NPU.

• Model Architecture: Swin Transformer — Base
• Source Model: timm/swin_base_patch4_window7_224.ms_in22k_ft_in1k — OpenMMLab config swin_base_in21k_pre_3rdparty_in1k
• Task: Image Classification — ImageNet-21k pretrained, ImageNet-1k fine-tuned (1000 classes)
• Parameters: 88M

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.

swin_base_in21k_pre_3rdparty_in1k.onnx (FP32)
        │
        └─▶  MWMX Runtime  ──▶  INT8 auto-cast  ──▶  NPX6 NPU

Provided Artifacts

Artifact	Status	Notes
FP32 (ONNX)	✅	fp32/swin-base_in21k-pre-3rdparty_in1k.onnx — FP32 ONNX export

Performance

Measured on Renesas R-Car X5H via the MWMX runtime (APM50 ship-performance CI pipeline).

Benchmark configuration: Single NPU · Batch size: 1 · Input resolution: not available from source data — TBD

Parameters	Runtime	Precision	Device	Latency (ms)	Type
88M	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 1 Core · 850 MHz	107.510244	Measured
88M	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 12 Cores · 850 MHz	34.342939	Measured

Accuracy

TBD — not yet measured/published for this repo.

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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

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Prerequisites

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

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

Download

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

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Benchmark Methodology

• HIL runs: Hardware-in-the-loop — measured on physical R-Car X5H silicon via the MWMX runtime (metawaremx_runtime CI pipeline, "APM50" ship-performance target)
• Precision: FP32 ONNX input; INT8 execution (auto-cast by MWMX)
• Slices: results reported for both 1 AI core and 12 AI cores per NPU instance