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ResNet50-v1-FHD-ONNX
Renesas/ResNet50-v1-FHD-ONNX
ResNet50-v1-FHD-ONNX is a image classification model from Renesas. Use it when you need a label for an image. The card lists the license as apache-2.0.
This repository hosts ResNet50-v1 (FHD), targeting the Renesas R-Car X5H platform for image-classification inference on the NPX6 NPU.
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Public
Updated Oct 2, 2026
Repo size
103 MB
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.onnx103 MB · 100%
At a glance
- Task
- Image Classification
- License
- apache-2.0
- Access
- Public
- Created
- Sep 23, 2026
- Updated
- Oct 2, 2026
- SHA
- 067353b1
Base models
Model card
From the Hugging Face model README
ResNet50-v1 (FHD) (ONNX) – Renesas X5H
Introduction
This repository hosts ResNet50-v1 (FHD), targeting the Renesas R-Car X5H platform for image-classification inference on the NPX6 NPU.
- Model Architecture: ResNet50 — the same 50-layer residual convolutional network as the base ResNet50-ONNX repo (identical resnet50-v1-12 ONNX Model Zoo checkpoint), compiled here against an FHD (Full-HD) NPU compile-target configuration
- Source Model: onnxmodelzoo/resnet50-v1-12
- Task: image-classification (dataset: imagenet-1k)
- Note: "FHD" refers to this variant's Full-HD (1920x1080) input resolution — unlike the base ResNet50-ONNX repo, which uses the standard 224x224 ImageNet crop, this repo benchmarks the same resnet50-v1-12 ONNX Model Zoo checkpoint compiled for a 1920x1080 input. This repo is a distinct full-HD-input benchmark variant of ResNet50-v1, separate from the base ResNet50-ONNX repo.
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.
resnet50-v1-12.onnx (FP32)
│
└─▶ MWMX Runtime ──▶ INT8 auto-cast ──▶ NPX6 NPU
Provided Artifacts
| Artifact | Status | Notes |
|---|---|---|
| FP32 (ONNX) | ⏳ Pending | fp32/resnet50-v1-12.onnx — to be added; will be auto-cast to INT8 by the MWMX toolchain at compile time (see Deployment Flow above); no separate INT8 file will be 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 × 1080 × 1920
| AI Cores | Runtime | Precision | Device | Latency (ms) | Type |
|---|---|---|---|---|---|
| 1 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 1 Core · 850 MHz | 64.51 | Measured |
| 3 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 3 Core · 850 MHz | 24.10 | Measured |
| 4 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 4 Core · 850 MHz | 18.56 | Measured |
| 6 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 6 Core · 850 MHz | 13.73 | Measured |
| 12 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 12 Core · 850 MHz | 8.51 | 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:
- Renesas R-Car X5H board with NPX6 NPU
- Renesas MWMX Runtime
- Hugging Face CLI to download the model
Download
hf download Renesas/ResNet50-v1-FHD-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_runtimeCI pipeline, "APM80" ship-performance target) - Precision: FP32 ONNX input; INT8 execution (auto-cast by MWMX)
- Task
- Image Classification
- License
- apache-2.0
- Created
- Sep 23, 2026
- Updated
- Oct 2, 2026