| ---
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| license: apache-2.0
|
| base_model: []
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| pipeline_tag: image-segmentation
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| tags:
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| - semantic-segmentation
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| - computer-vision
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| - renesas
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| - x5h
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| - onnx
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| - deeplabv3plus
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| - resnet50
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| - cityscapes
|
| ---
|
|
|
| # DeepLabV3Plus-R50 (ONNX) – Renesas X5H
|
|
|
| > **⚠️ Partial-model caveat.** The source checkpoint name ends in
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| > `custom_seg_split_4_split_2` — this artifact is **one segment of a 4-way split network**, not
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| > the full end-to-end DeepLabV3+ model. The latency below reflects only that segment; do not
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| > quote it as whole-model latency until the other splits are accounted for.
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|
|
| ## Introduction
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|
|
| This repository hosts **DeepLabV3+ (ResNet50-D8 backbone)** targeting the **Renesas R-Car X5H**
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| platform for semantic segmentation inference on the NPX6 NPU.
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|
|
| - **Model Architecture:** DeepLabV3+ with ResNet50-D8 backbone
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| - **Source Model:** OpenMMLab config [`deeplabv3plus_r50_d8_4xb2_80k_cityscapes_512x1024`](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/metafile.yaml)
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| *(no HuggingFace mirror of these weights; see `model.source` in `.metadata.yaml`)*
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| - **Task:** Semantic Segmentation
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| - **Dataset:** Cityscapes (inferred from checkpoint name)
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| - **Input Resolution:** 512 × 1024 (explicit in checkpoint name)
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| - **Parameters:** not published — count them from the ONNX graph (`sum(numpy_helper.to_array(t).size for t in model.graph.initializer)`)
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|
|
| ## Deployment Flow
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|
|
| The FP32 ONNX model is auto-cast to **INT8** by the Renesas MWMX toolchain at compile time — no
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| separate quantization step is required.
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|
|
| ```
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| deeplabv3plus_r50_oss_sim_inf.onnx (FP32, split_2 segment)
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| │
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| └─▶ MWMX Runtime ──▶ INT8 auto-cast ──▶ NPX6 NPU
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| ```
|
|
|
| ## Provided Artifacts
|
|
|
| | Artifact | Status | Notes |
|
| |----------|--------|-------|
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| | **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).
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|
|
| > **Benchmark configuration:** Single NPU · Batch size: 1 · Input: 3 × 512 × 1024
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| >
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| > The 1-AI-core slice failed to compile in the source CI pipeline, so only the 12-core result
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| > is available. Latency is for the `split_2` segment only (see caveat above).
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|
|
| | Runtime | Precision | Device | Latency (ms) | Type |
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| |---------|-----------|--------|---------------|------|
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| | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 12 Cores · 850 MHz | 38.104 | Measured |
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|
|
| > Reconfirmed: 1-core compile still fails as of the 2026-09-16 benchmark run.
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|
|
| ### Accuracy (mIoU)
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|
|
| TBD — not yet measured/published for this repo.
|
|
|
| ---
|
|
|
| ## Runtime Details
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|
|
| ### MWMX Runtime
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|
|
| - **Engine:** Renesas MWMX (Middleware MX) native inference runtime
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| - **Input format:** FP32 ONNX (compiled by the MWMX toolchain)
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| - **NPU execution precision:** INT8 (auto-cast by MWMX toolchain)
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| - **Execution target:** NPX6-48K NPU on R-Car X5H
|
|
|
| ---
|
|
|
| ## Prerequisites
|
|
|
| To run inference on Renesas R-Car X5H, you need:
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|
|
| 1. **Renesas R-Car X5H board** with NPX6 NPU
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| 2. **Renesas MWMX Runtime**
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| 3. **Hugging Face CLI** to download the model
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|
|
| ## Download
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|
|
| ```bash
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| hf download Renesas/DeepLabV3Plus-R50-ONNX --repo-type=model --include "fp32/*"
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| ```
|
|
|
| ---
|
|
|
| ## Benchmark Methodology
|
|
|
| - **HIL runs:** Hardware-in-the-loop — measured on physical R-Car X5H silicon via the MWMX
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| runtime (`metawaremx_runtime` CI pipeline, "APM80" ship-performance target)
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| - **Precision:** FP32 ONNX input; INT8 execution (auto-cast by MWMX)
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| - **Slices:** only the 12-AI-core result is available (1-core compile failed)
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| - **Scope:** this artifact is a single segment (`split_2` of 4) of the full segmentation pipeline
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|
|
