---
license: apache-2.0
base_model: []
pipeline_tag: object-detection
tags:
- object-detection
- computer-vision
- renesas
- x5h
- onnx
- tao
- resnet34
---

# TAO-PeopleNet-R34 (ONNX) – Renesas X5H

## Introduction

This repository hosts **TAO-PeopleNet-R34**, targeting the **Renesas R-Car X5H** platform for
object detection inference on the NPX6 NPU.

> **Note:** The other TAO networks each have their own sibling repo (`TAO-DashCamNet-R18-ONNX`, `TAO-PeopleNet-R34-ONNX`, `TAO-TrafficCamNet-R18-ONNX`). They are separate products trained on separate data, not precisions of one model.

- **Model Architecture:** TAO-PeopleNet-R34 — resnet34 backbone
- **Source Model:** NVIDIA NGC [`resnet34_peoplenet`](https://catalog.ngc.nvidia.com/orgs/nvidia/models/peoplenet)
  *(no HuggingFace mirror of these weights; see `model.source` in `.metadata.yaml`)*
- **Task:** Object Detection
- **Parameters:** not published — count them from the ONNX graph (`sum(numpy_helper.to_array(t).size for t in model.graph.initializer)`)

## 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.

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

## Provided Artifacts

| Artifact | Status | Notes |
|----------|--------|-------|
| **FP32 (ONNX)** | ✅ | `fp32/resnet34_peoplenet.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

| Runtime      | Precision    | Device                             | Latency (ms) | Type     |
| :----------: | :----------: | :---------------------------------: | :----------: | :------: |
| MWMX Runtime | INT8 (auto)  | X5H · 1× NPU · 12 Cores · 850 MHz | 2.781941 | Measured |

### Accuracy

TBD — not yet measured/published for this repo.

---

## 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

---

## 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

```bash
hf download Renesas/TAO-PeopleNet-R34-ONNX --repo-type=model --include "fp32/*"
```

---

## 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 are reported per NPU core count where both runs compiled