| --- |
| license: apache-2.0 |
| base_model: |
| - onnx/resnet50 |
| pipeline_tag: image-classification |
| tags: |
| - image-classification |
| - computer-vision |
| - renesas |
| - x5h |
| - onnx |
| - resnet |
| --- |
| |
| # ResNet50 (ONNX) – Renesas X5H |
|
|
| ## Introduction |
|
|
| This repository hosts **ResNet50 V1** in ONNX FP32 format, targeting the **Renesas R-Car X5H** platform for image classification inference on the NPX6 NPU. |
|
|
| - **Model Architecture:** ResNet50 — a 50-layer residual convolutional network for 1000-class image classification. |
| - **Source Model:** [onnx/resnet50](https://huggingface.co/onnx/resnet50) (ONNX Model Zoo v1.12) |
| - **Task:** Image Classification (ImageNet ILSVRC2012, 1000 classes) |
| - **Parameters:** 25.6 M |
|
|
| ## Deployment Flow |
|
|
| The repository provides the model in **FP32 ONNX** format. Both supported runtimes automatically cast the FP32 model to **INT8** at load time for optimised NPU execution — no separate quantization step is required. |
|
|
| ``` |
| resnet50_v1_12.onnx (FP32) |
| │ |
| ├─▶ ONNX Runtime (Custom NPU EP) ──▶ INT8 auto-cast ──▶ NPX6 NPU |
| │ |
| └─▶ MWMX Runtime ──▶ INT8 auto-cast ──▶ NPX6 NPU |
| ``` |
|
|
| ## Provided Artifacts |
|
|
| | Artifact | Status | Notes | |
| | :-------------: | :----------: | -------------------------------------------- | |
| | **FP32 (ONNX)** | ✅ Provided | Reference baseline from ONNX Model Zoo v1.12 | |
|
|
| > INT8 execution is handled automatically by the NPU runtime — no additional quantized model file is needed. |
|
|
| ## Performance |
|
|
| All HIL results were measured on **Renesas R-Car X5H** physical hardware. |
| The FP32 ONNX model is auto-cast to INT8 by the runtime before NPU execution. |
| PPA Estimator results are software estimates based on model characteristics and hardware configuration. |
|
|
| > **Benchmark configuration:** Single NPU · Single AI Core · Input: 3 × 224 × 224 · Batch size: 1 |
|
|
| ### Inference Latency & Throughput |
|
|
| | Runtime | Precision | Device | Latency (ms) | Throughput (fps) | Type | |
| | :----------------------: | :---------: | :---------------------------: | :----------: | :--------------: | :-------: | |
| | ORT Custom NPU EP | INT8 (auto) | X5H · 1× NPU · 1 Core · 850 MHz | 4.54 | 243.9 | Measured | |
| | MWMX Runtime | INT8 (auto) | X5H · 1× NPU · 1 Core · 850 MHz | 3.23 | 303.0 | Measured | |
| | PPA Estimator | INT8 | X5H · 1× NPU · 1 Core · 1066 MHz | 5.9 | — | Estimated | |
|
|
| ### Accuracy (ImageNet ILSVRC2012 Validation Set — 50 000 images) |
|
|
| | Runtime / Precision | Top-1 Accuracy | Top-5 Accuracy | Notes | |
| | :----------------------: | :------------: | :------------: | ----------------------------- | |
| | FP32 reference | 81.3 % | 93.9 % | ORT, FP32 native execution | |
| | ORT Custom NPU EP (INT8) | 73.0 % | 94.0 % | INT8 auto-cast, NPU execution | |
| | MWMX Runtime (INT8) | — | — | Not yet measured | |
|
|
| --- |
|
|
| ## Runtime Details |
|
|
| ### ONNX Runtime – Custom NPU Execution Provider |
|
|
| - **Engine:** ONNX Runtime with Renesas Custom NPU Execution Provider |
| - **Input format:** FP32 ONNX (`.onnx`) |
| - **NPU execution precision:** INT8 (auto-cast at load time) |
| - **Execution target:** NPX6-48K NPU on R-Car X5H |
|
|
| ### MWMX Runtime |
|
|
| - **Engine:** Renesas MWMX (Middleware MX) native inference runtime |
| - **Input format:** FP32 ONNX (ingested and compiled by the MWMX toolchain) |
| - **NPU execution precision:** INT8 (auto-cast by MWMX toolchain) |
| - **Execution target:** NPX6-48K NPU on R-Car X5H |
|
|
| ### PPA Estimator |
|
|
| - **Engine:** Renesas PPA Estimator |
| - **Input format:** FP32 ONNX |
| - **NPU execution precision:** INT8 |
| - **Type:** Software performance estimate — not measured on physical silicon |
|
|
| --- |
|
|
| ## Prerequisites |
|
|
| To run inference on Renesas R-Car X5H, you need: |
|
|
| 1. **Renesas R-Car X5H board** with NPX6 NPU |
| 2. **ONNX Runtime** with Renesas NPU Custom Execution Provider, **or** the **Renesas MWMX Runtime** package |
| 3. **Hugging Face CLI** to download the model |
|
|
| ## Download |
|
|
| ```bash |
| huggingface-cli download Renesas/Resnet50-ONNX fp32/resnet50_v1_12.onnx |
| ``` |
|
|
| ## Inference |
|
|
| ### ONNX Runtime (Custom NPU Execution Provider) |
|
|
| ```python |
| import onnxruntime as ort |
| import numpy as np |
| |
| # Runtime auto-casts FP32 model to INT8 for NPU execution |
| providers = [("ReneasNPUExecutionProvider", {}), "CPUExecutionProvider"] |
| sess = ort.InferenceSession("fp32/resnet50_v1_12.onnx", providers=providers) |
| |
| # Input: ImageNet-normalized image, shape (1, 3, 224, 224), dtype float32 |
| input_data = np.random.randn(1, 3, 224, 224).astype(np.float32) |
| outputs = sess.run(None, {"data": input_data}) |
| class_scores = outputs[0] # shape (1, 1000) |
| ``` |
|
|
| ### MWMX Runtime |
|
|
| Refer to the Renesas MWMX Runtime documentation for compilation and inference scripts targeting the NPX6 NPU on R-Car X5H. The MWMX toolchain ingests the FP32 ONNX model and automatically compiles it for INT8 NPU execution. |
|
|
| --- |
|
|
| ## Benchmark Methodology |
|
|
| - **HIL runs:** Hardware-in-the-loop — measured on physical R-Car X5H silicon; single NPU, single AI core, 850 MHz NPU clock |
| - **Estimation:** PPA Estimator software estimate; single NPU, single AI core, 1066 MHz NPU clock |
| - **Precision:** FP32 ONNX input; INT8 execution (auto-cast by runtime) |
| - **Latency:** Median over 1000 consecutive inference runs with warm cache |
| - **Throughput:** Computed as `1000 / latency_ms` |
| - **Accuracy:** Evaluated on the ImageNet ILSVRC2012 validation set (50 000 images); softmax Top-1 and Top-5 |
|
|
