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license: apache-2.0
base_model: []
pipeline_tag: object-detection
tags:
- object-detection
- computer-vision
- renesas
- x5h
- onnx
- centernet
- resnet18
- detection
---
# CenterNet-R18 (ONNX) – Renesas X5H
## Introduction
This repository hosts **CenterNet** with a **ResNet18** backbone, targeting the **Renesas
R-Car X5H** platform for object detection inference on the NPX6 NPU.
- **Model Architecture:** CenterNet — keypoint-based, anchor-free object detector, ResNet18 backbone
- **Source Model:** OpenMMLab config [`centernet_resnet18_140e_coco`](https://github.com/open-mmlab/mmdetection/blob/main/configs/centernet/metafile.yml)
*(no HuggingFace mirror of these weights; see `model.source` in `.metadata.yaml`)*
- **Task:** Object Detection
- **Dataset:** COCO (inferred from checkpoint name)
- **Input Resolution:** 512 × 512 (inferred from `crop512` in the 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.
```
centernet_r18_..._optimized.onnx (FP32)
│
└─▶ MWMX Runtime ──▶ INT8 auto-cast ──▶ NPX6 NPU
```
## Provided Artifacts
| Artifact | Status | Notes |
|----------|--------|-------|
| **FP32 (ONNX)** | ✅ Published | `fp32/centernet_r18_8xb16_crop512_140e_coco.onnx` — 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 × 512 (inferred)
| Runtime | Precision | Device | Latency (ms) | Type |
|---------|-----------|--------|---------------|------|
| MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 1 Core · 850 MHz | 11.978 | Measured |
| MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 1 Core · 850 MHz | 11.989 | Measured (2026-09-16) |
| MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 12 Cores · 850 MHz | 3.467 | 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/CenterNet-R18-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:** results reported for both 1 AI core and 12 AI cores per NPU instance
|