hrnetv2

AMD-PAVS-AI/hrnetv2

hrnetv2 is a image segmentation model from AMD-PAVS-AI. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for onnx. The card lists the license as mit.

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Updated Aug 4, 2026

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At a glance

Task: Image Segmentation
Library: onnx
License: mit
Access: Public
Created: Aug 4, 2026
Updated: Aug 4, 2026
SHA: 8c5a3736

Model card

From the Hugging Face model README

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. This repository contains configurations and scripts optimized for AMD® ROCm™ platforms. You can use the hrnetv2 AMD scripts to reproduce results or export with custom configurations. More details on model performance can be found here.

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.

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:

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

Dig Deeper

Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples?

📂 View the full project on GitHub

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)

Task: Image Segmentation
Library: onnx
License: mit
Created: Aug 4, 2026
Updated: Aug 4, 2026