---
license: apache-2.0
base_model:
- onnx/resnet50
pipeline_tag: image-classification
tags:
- image-classification
- computer-vision
- renesas
- x5h
- onnx
- resnet
---

# 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](https://huggingface.co/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:

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

## Download

```bash
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