| --- |
| license: apache-2.0 |
| base_model: |
| - open-mmlab/mmpretrain |
| pipeline_tag: image-classification |
| tags: |
| - image-classification |
| - computer-vision |
| - renesas |
| - x5h |
| - onnx |
| - hrnet |
| --- |
| |
| # HRNet-W48 (ONNX) – Renesas X5H |
|
|
| ## Introduction |
|
|
| This repository hosts **HRNet-W48**, targeting the **Renesas R-Car X5H** platform for |
| image-classification inference on the NPX6 NPU. |
|
|
| - **Model Architecture:** HRNet (High-Resolution Network, W48 width) — maintains high-resolution representations throughout the network via parallel multi-resolution branches with repeated cross-resolution fusion, instead of the classic encoder-decoder downsample/upsample path |
| - **Source Model:** [open-mmlab/mmpretrain](https://github.com/open-mmlab/mmpretrain/blob/main/configs/hrnet/hrnet-w48_4xb32_in1k.py) |
| - **Task:** image-classification (dataset: imagenet-1k) |
| - **Note:** This ImageNet-1k classification checkpoint uses the HRNet backbone with a classification head; HRNet is also widely used as a backbone for pose estimation and segmentation. |
|
|
| ## 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. |
|
|
| ``` |
| hrnet_w48_3rdparty_8xb32_in1k.onnx (FP32) |
| │ |
| └─▶ MWMX Runtime ──▶ INT8 auto-cast ──▶ NPX6 NPU |
| ``` |
|
|
| ## Provided Artifacts |
|
|
| | Artifact | Status | Notes | |
| |----------|--------|-------| |
| | **FP32 (ONNX)** | ⏳ Pending | `fp32/hrnet_w48_3rdparty_8xb32_in1k.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 × 224 × 224 |
|
|
| | AI Cores | Runtime | Precision | Device | Latency (ms) | Type | |
| | :------: | :----------: | :----------: | :---------------------------------: | :----------: | :------: | |
| | 1 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 1 Core · 850 MHz | 14.40 | Measured | |
| | 3 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 3 Core · 850 MHz | 9.70 | Measured | |
| | 4 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 4 Core · 850 MHz | 9.58 | Measured | |
| | 6 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 6 Core · 850 MHz | 9.90 | Measured | |
| | 12 | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 12 Core · 850 MHz | 11.36 | 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/HRNet-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) |
|
|
