---
license: apache-2.0
base_model:
- onnxmodelzoo/retinanet-9
pipeline_tag: object-detection
tags:
- object-detection
- computer-vision
- renesas
- x5h
- onnx
- retinanet
- resnet101
- detection
---

# RetinaNet-R101 (ONNX) – Renesas X5H

## Introduction

This repository hosts **RetinaNet** in ONNX FP32 format, targeting the **Renesas R-Car X5H** platform for object detection inference on the NPX6 NPU.

- **Model Architecture:** RetinaNet with ResNet101 backbone and Feature Pyramid Network (FPN)
- **Source Model:** [onnxmodelzoo/retinanet-9](https://huggingface.co/onnxmodelzoo/retinanet-9) — ONNX Model Zoo [`retinanet-9`](https://github.com/onnx/models)
- **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:

1. Anchor generation
2. Bounding box decoding
3. Confidence threshold filtering
4. 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:

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

## Download

```bash
hf download Renesas/RetinaNet-R101-ONNX --repo-type=model --include "fp32/*"
```

## 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