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DeepLabV3Plus-R50-ONNX

Image Segmentation
ONNX
semantic-segmentation
computer-vision
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deeplabv3plus
resnet50
cityscapes
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DeepLabV3Plus-R50-ONNX / README.md
Artem Plastinkin
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metadata 
license: apache-2.0
base_model: []
pipeline_tag: image-segmentation
tags:
  - semantic-segmentation
  - computer-vision
  - renesas
  - x5h
  - onnx
  - deeplabv3plus
  - resnet50
  - cityscapes

DeepLabV3Plus-R50 (ONNX) – Renesas X5H

⚠️ Partial-model caveat. The source checkpoint name ends in custom_seg_split_4_split_2 — this artifact is one segment of a 4-way split network, not the full end-to-end DeepLabV3+ model. The latency below reflects only that segment; do not quote it as whole-model latency until the other splits are accounted for.

Introduction

This repository hosts DeepLabV3+ (ResNet50-D8 backbone) targeting the Renesas R-Car X5H platform for semantic segmentation inference on the NPX6 NPU.

  • Model Architecture: DeepLabV3+ with ResNet50-D8 backbone
  • Source Model: OpenMMLab config deeplabv3plus_r50_d8_4xb2_80k_cityscapes_512x1024 (no HuggingFace mirror of these weights; see model.source in .metadata.yaml)
  • Task: Semantic Segmentation
  • Dataset: Cityscapes (inferred from checkpoint name)
  • Input Resolution: 512 × 1024 (explicit in checkpoint name)
  • 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.

deeplabv3plus_r50_oss_sim_inf.onnx (FP32, split_2 segment)
        │
        └─▶  MWMX Runtime  ──▶  INT8 auto-cast  ──▶  NPX6 NPU

Provided Artifacts

Artifact Status Notes
FP32 (ONNX) ✅ Published fp32/deeplabv3plus_r50_oss_sim_inf.onnx — segment split_2 of 4; auto-cast to INT8 by the MWMX toolchain at compile time (see Deployment Flow above); no separate INT8 file is shipped

Performance

Measured on Renesas R-Car X5H via the MWMX runtime (APM80 ship-performance CI pipeline).

Benchmark configuration: Single NPU · Batch size: 1 · Input: 3 × 512 × 1024

The 1-AI-core slice failed to compile in the source CI pipeline, so only the 12-core result is available. Latency is for the split_2 segment only (see caveat above).

Runtime Precision Device Latency (ms) Type
MWMX Runtime INT8 (auto) X5H · 1× NPU · 12 Cores · 850 MHz 38.104 Measured

Reconfirmed: 1-core compile still fails as of the 2026-09-16 benchmark run.

Accuracy (mIoU)

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/DeepLabV3Plus-R50-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, "APM80" ship-performance target)
  • Precision: FP32 ONNX input; INT8 execution (auto-cast by MWMX)
  • Slices: only the 12-AI-core result is available (1-core compile failed)
  • Scope: this artifact is a single segment (split_2 of 4) of the full segmentation pipeline