Model Architecture: RetinaNet with ResNet101 backbone and Feature Pyramid Network (FPN)
Source Model: ONNX Model Zoo RetinaNet
Task: Object Detection
Dataset: COCO
Accuracy: mAP = 0.376
Backbone: ResNet101

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.

text

retinanet-9.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 model from ONNX Model Zoo

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 × 480 × 640 · 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	TBD	TBD	Measured
MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 1 Core · 850 MHz	TBD	TBD	Measured
PPA Estimator	INT8	X5H · 1× NPU · 1 Core · 1066 MHz	TBD	—	Estimated

Accuracy (COCO Validation Set)

Runtime / Precision	mAP (IoU=0.50:0.95)	Notes
FP32 Reference	0.376	ONNX Model Zoo reference
ORT Custom NPU EP (INT8)	TBD	NPU execution
MWMX Runtime (INT8)	TBD	NPU execution

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

Model Input

Input Tensor

Shape: (N, 3, H, W)
Format: RGB
Data Type: FP32
Pixel Range: [0, 1]

Preprocessing

python

from torchvision import transforms

preprocess = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    ),
])

Model Outputs

The model produces 10 output tensors corresponding to RetinaNet's multi-scale detection heads.

Classification Heads

Five tensors corresponding to object classification on feature pyramid levels P3–P7.

Example shapes for an input image of size 1 × 3 × 480 × 640:

text

[1, 720, 60, 80]
[1, 720, 30, 40]
[1, 720, 15, 20]
[1, 720, 8, 10]
[1, 720, 4, 5]

Bounding Box Regression Heads

Five tensors corresponding to anchor-box regression outputs.

text

[1, 36, 60, 80]
[1, 36, 30, 40]
[1, 36, 15, 20]
[1, 36, 8, 10]
[1, 36, 4, 5]

Postprocessing

RetinaNet requires the following postprocessing steps:

Anchor generation
Bounding box decoding
Confidence threshold filtering
Non-Maximum Suppression (NMS)

These steps produce the final object detections:

Bounding boxes
Confidence scores
Class labels

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
Hugging Face CLI to download the model

Download

huggingface-cli download Renesas/RetinaNet-ONNX fp32/retinanet-9.onnx

Inference

ONNX Runtime (Custom NPU Execution Provider)

python

import onnxruntime as ort
import numpy as np

providers = [
    ("RenesasNPUExecutionProvider", {}),
    "CPUExecutionProvider"
]

sess = ort.InferenceSession(
    "fp32/retinanet-9.onnx",
    providers=providers
)

input_data = np.random.rand(
    1, 3, 480, 640
).astype(np.float32)

outputs = sess.run(
    None,
    {"images": input_data}
)

# outputs[0:5] -> classification heads
# outputs[5:10] -> box regression heads

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 using the COCO validation dataset
Postprocessing: Includes anchor generation, bounding-box decoding, confidence filtering, and NMS

Renesas/Retinanet-ONNX

RetinaNet (ONNX) – Renesas X5H

Introduction

Deployment Flow

Provided Artifacts

Performance

Inference Latency & Throughput

Accuracy (COCO Validation Set)

Runtime Details

ONNX Runtime – Custom NPU Execution Provider

MWMX Runtime

PPA Estimator

Model Input

Input Tensor

Preprocessing

Model Outputs

Classification Heads

Bounding Box Regression Heads

Postprocessing

Prerequisites

Download

Inference

ONNX Runtime (Custom NPU Execution Provider)

MWMX Runtime

Benchmark Methodology

Renesas/
Retinanet-ONNX