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Renesas/
Resnet50-ONNX

Name: Resnet50-ONNX
Availability: InStock

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ResNet50 (ONNX) – Renesas X5H

Introduction

This repository hosts ResNet50 V1 in ONNX FP32 format, targeting the Renesas R-Car X5H platform for image classification inference on the NPX6 NPU.

Model Architecture: ResNet50 — a 50-layer residual convolutional network for 1000-class image classification.
Source Model: onnx/resnet50 (ONNX Model Zoo v1.12)
Task: Image Classification (ImageNet ILSVRC2012, 1000 classes)
Parameters: 25.6 M

Deployment Flow

The repository provides the model in FP32 ONNX format. Both supported runtimes automatically cast the FP32 model to INT8 at load time for optimised NPU execution — no separate quantization step is required.

resnet50_v1_12.onnx (FP32)
        │
        ├─▶  ONNX Runtime (Custom NPU EP)  ──▶  INT8 auto-cast  ──▶  NPX6 NPU
        │
        └─▶  MWMX Runtime                  ──▶  INT8 auto-cast  ──▶  NPX6 NPU

Provided Artifacts

Artifact	Status	Notes
FP32 (ONNX)	✅ Provided	Reference baseline from ONNX Model Zoo v1.12

INT8 execution is handled automatically by the NPU runtime — no additional quantized model file is needed.

Performance

All HIL results were measured on Renesas R-Car X5H physical hardware.
The FP32 ONNX model is auto-cast to INT8 by the runtime before NPU execution.
PPA Estimator results are software estimates based on model characteristics and hardware configuration.

Benchmark configuration: Single NPU · Single AI Core · Input: 3 × 224 × 224 · Batch size: 1

Inference Latency & Throughput

Runtime	Precision	Device	Latency (ms)	Throughput (fps)	Type
ORT Custom NPU EP	INT8 (auto)	X5H · 1× NPU · 1 Core · 850 MHz	4.54	243.9	Measured
MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 1 Core · 850 MHz	3.23	303.0	Measured
PPA Estimator	INT8	X5H · 1× NPU · 1 Core · 1066 MHz	5.9	—	Estimated

Accuracy (ImageNet ILSVRC2012 Validation Set — 50 000 images)

Runtime / Precision	Top-1 Accuracy	Top-5 Accuracy	Notes
FP32 reference	81.3 %	93.9 %	ORT, FP32 native execution
ORT Custom NPU EP (INT8)	73.0 %	94.0 %	INT8 auto-cast, NPU execution
MWMX Runtime (INT8)	—	—	Not yet measured

Runtime Details

ONNX Runtime – Custom NPU Execution Provider

Engine: ONNX Runtime with Renesas Custom NPU Execution Provider
Input format: FP32 ONNX (.onnx)
NPU execution precision: INT8 (auto-cast at load time)
Execution target: NPX6-48K NPU on R-Car X5H

MWMX Runtime

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

PPA Estimator

Engine: Renesas PPA Estimator
Input format: FP32 ONNX
NPU execution precision: INT8
Type: Software performance estimate — not measured on physical silicon

Prerequisites

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

Renesas R-Car X5H board with NPX6 NPU
ONNX Runtime with Renesas NPU Custom Execution Provider, or the Renesas MWMX Runtime package
Hugging Face CLI to download the model

Download

huggingface-cli download Renesas/Resnet50-ONNX fp32/resnet50_v1_12.onnx

Inference

ONNX Runtime (Custom NPU Execution Provider)

python

import onnxruntime as ort
import numpy as np

# Runtime auto-casts FP32 model to INT8 for NPU execution
providers = [("ReneasNPUExecutionProvider", {}), "CPUExecutionProvider"]
sess = ort.InferenceSession("fp32/resnet50_v1_12.onnx", providers=providers)

# Input: ImageNet-normalized image, shape (1, 3, 224, 224), dtype float32
input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)
outputs = sess.run(None, {"data": input_data})
class_scores = outputs[0]  # shape (1, 1000)

MWMX Runtime

Refer to the Renesas MWMX Runtime documentation for compilation and inference scripts targeting the NPX6 NPU on R-Car X5H. The MWMX toolchain ingests the FP32 ONNX model and automatically compiles it for INT8 NPU execution.

Benchmark Methodology

HIL runs: Hardware-in-the-loop — measured on physical R-Car X5H silicon; single NPU, single AI core, 850 MHz NPU clock
Estimation: PPA Estimator software estimate; single NPU, single AI core, 1066 MHz NPU clock
Precision: FP32 ONNX input; INT8 execution (auto-cast by runtime)
Latency: Median over 1000 consecutive inference runs with warm cache
Throughput: Computed as 1000 / latency_ms
Accuracy: Evaluated on the ImageNet ILSVRC2012 validation set (50 000 images); softmax Top-1 and Top-5