| --- |
| library_name: onnx |
| license: mit |
| tags: |
| - foundation |
| - amd |
| - rocm |
| - image-segmentation |
| pipeline_tag: image-segmentation |
| --- |
| |
|  |
|
|
| # HRNetv2-W48: Optimized for AMD ROCm |
|
|
| HRNetv2-W48 is a semantic segmentation model that assigns a class label to every pixel while maintaining high-resolution feature representations throughout the network. This repository packages inference for semantic segmentation using **ONNX Runtime**, exported and validated for **AMD ROCm** so it runs efficiently on AMD GPUs, CPUs, and NPUs. |
|
|
| This is based on the implementation of HRNetv2 found [here](https://github.com/HRNet/HRNet-Semantic-Segmentation). |
| This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [hrnetv2 AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/hrnetv2) to reproduce results or export with custom configurations. More details on model performance can be found [here](#accuracy-pipeline). |
|
|
| --- |
|
|
| ## Task Overview |
|
|
| **Task:** Semantic segmentation |
|
|
| **Dataset:** Cityscapes val (500 images) · ADE20k val · LIP val (variant-specific) |
|
|
| **Output metrics:** mIoU (mean Intersection over Union), pixel accuracy, mean class accuracy |
|
|
| > **Model variants:** Default is **Cityscapes** at 1024×2048 (paper mIoU 80.9%). Override with `HRNET_VARIANT` or `HRNET_MODEL` (e.g. `export HRNET_MODEL=ade20k`) or run `make set-variant VARIANT=MODEL_LIP` for a persistent override. Run `make list-variants` for aliases. |
|
|
| > **NPU note:** NPU float precisions (FP32/FP16/BF16) use per-dtype `config/vitisai_config_*.json`; NPU INT8 requires `make quantize-npu-int8` before benchmark/eval. |
| |
| --- |
| |
| ## AMD ROCm Optimization |
| |
| This model export has been adapted and validated for **AMD Instinct™ / Radeon™ GPUs** running **ROCm**, as well as AMD CPUs and AMD Ryzen AI NPUs. Key points: |
| |
| - Validated backends: **ONNX Runtime** across CPU, GPU (MIGraphX execution provider), and NPU (VitisAI execution provider). |
| - No code changes required versus the upstream HRNet implementation — only environment/runtime configuration differs. |
| - CPU fallback path supported for environments without a ROCm-capable GPU. |
| |
| | Runtime | Precision | Backend | Hardware | Notes | |
| |---|---|---|---|---| |
| | ONNX Runtime | FP32 / FP16 / BF16 / INT8 | CPU Execution Provider | AMD CPU | — | |
| | ONNX Runtime | FP32 / FP16 / BF16 / INT8 | MIGraphX Execution Provider | AMD Instinct™ / Radeon™ GPU (ROCm) | — | |
| | ONNX Runtime | FP32 / FP16 / BF16 / INT8 | VitisAI Execution Provider | AMD Ryzen AI NPU | INT8 requires Quark calibration via `make quantize-npu-int8` | |
| |
| --- |
| |
| ## Getting Started |
| |
| For setup instructions, evaluation scripts, and custom configuration options, see the [hrnetv2 on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/hrnetv2). |
| |
| --- |
| |
| ## Model Details |
| |
| **Model Type:** Semantic segmentation, HRNetv2-W48 |
| |
| **Base Model:** `HRNet/HRNet-Semantic-Segmentation` (HRNetv2-W48) |
| |
| **Model Stats:** |
| - Model variant: Cityscapes, 1024×2048 input (default) — ADE20k (520×520) and LIP (473×473) also supported |
| - Precision tested: FP32, FP16, BF16, INT8 |
| |
| --- |
| |
| ## Accuracy Pipeline |
| |
| Higher mIoU means predicted pixel labels agree more closely with ground truth — 100% is perfect overlap, 0% is no agreement. |
| |
| ### Metrics Explained |
| |
| | Metric | Description | |
| |--------|-------------| |
| | mIoU | Primary segmentation metric — mean Intersection-over-Union averaged across all classes. Higher means better boundary alignment and class assignment across the validation set. | |
| | Pixel accuracy | Fraction of correctly labeled pixels — rewards overall coverage but can hide poor performance on rare classes. | |
| | Mean class accuracy | Average per-class accuracy — exposes imbalance when large classes dominate pixel accuracy. | |
| |
| ### Accuracy Results |
| |
| **Full Dataset Evaluation (Cityscapes val)** — filled from `evaluation_results/`; run `make metrics` to refresh: |
| |
| <!-- accuracy-table-start --> |
| | Device | Backend | Precision | Variant | Accuracy (%) | |
| |--------|---------|-----------|---------|--------------| |
| | CPU | ONNX Runtime | FP32 | Cityscapes | 40.54 | |
| | CPU | ONNX Runtime | FP16 | Cityscapes | 40.75 | |
| | CPU | ONNX Runtime | BF16 | Cityscapes | 40.82 | |
| | CPU | ONNX Runtime | INT8 | Cityscapes | 40.99 | |
| | GPU | ONNX Runtime | FP32 | Cityscapes | 40.86 | |
| | GPU | ONNX Runtime | FP16 | Cityscapes | 40.63 | |
| | GPU | ONNX Runtime | BF16 | Cityscapes | 40.67 | |
| | GPU | ONNX Runtime | INT8 | Cityscapes | 40.82 | |
| | NPU | ONNX Runtime | FP32 | Cityscapes | 40.49 | |
| | NPU | ONNX Runtime | FP16 | Cityscapes | 41.10 | |
| | NPU | ONNX Runtime | BF16 | Cityscapes | 40.92 | |
| | NPU | ONNX Runtime | INT8 | Cityscapes | 0.00 | |
| <!-- accuracy-table-end --> |
| |
| --- |
| |
| ## Dig Deeper |
| |
| Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples? |
| |
| 📂 **[View the full project on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/hrnetv2)** |
| |
| The GitHub repository includes: |
| - Benchmark, profile, and evaluation Makefile targets for CPU, GPU, and NPU |
| - Dataset staging for Cityscapes, ADE20k, and LIP variants |
| - ONNX export scripts and the patched upstream HRNet evaluation tooling |
| - Additional HRNetv2-W48 variants (Cityscapes, ADE20k, LIP) |
| |
