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TAO-PeopleNet-R34-ONNX

Object Detection
ONNX
computer-vision
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tao
resnet34
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Artem Plastinkin
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metadata 
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 (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 

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