---
library_name: onnx
license: apache-2.0
tags:
  - foundation
  - amd
  - rocm
  - image-segmentation
pipeline_tag: image-segmentation
---

![](https://huggingface.co/AMD-PAVS-AI/unet/resolve/main/unet.png)

# UNet: Optimized for AMD ROCm

UNet-S5-D16 is a convolutional encoder-decoder network for semantic segmentation — it classifies every pixel in an image into one of 19 urban-scene categories (road, sidewalk, building, person, car, etc.). 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 UNet found [here](https://github.com/open-mmlab/mmsegmentation).
This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [unet AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/unet) to reproduce results or export with custom configurations. More details on model performance can be found [here](#accuracy-pipeline).

---

## Task Overview

**Task:** Cityscapes semantic segmentation (19 classes)

**Dataset:** Cityscapes val split (500 images), downloaded automatically from [HuggingFace](https://huggingface.co/datasets/Antreas/Cityscapes); benchmark inputs from [UrbanSyn](https://huggingface.co/datasets/UrbanSyn/UrbanSyn)

**Output metrics:** mIoU (mean Intersection-over-Union across 19 classes), per-class IoU, pixel accuracy

---

## 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 (FP32), GPU (MIGraphX — FP32/FP16/BF16/INT8), and NPU (VitisAI — FP32/FP16/BF16/INT8).
- No code changes required versus the upstream UNet-S5-D16 (mmsegmentation) implementation — only environment/runtime configuration differs.
- All GPU precisions use a single FP32 ONNX model with runtime quantization via MIGraphX EP options.

| Runtime | Precision | Backend | Hardware | Notes |
|---|---|---|---|---|
| ONNX Runtime | FP32 | CPU Execution Provider | AMD CPU | — |
| ONNX Runtime | FP32 / FP16 / BF16 / INT8 | MIGraphX Execution Provider | AMD Instinct™ / Radeon™ GPU (ROCm) | First run may take 30+ minutes due to graph compilation |
| ONNX Runtime | FP32 / FP16 / BF16 / INT8 | VitisAI Execution Provider | AMD Ryzen AI NPU | INT8 requires Quark + Cityscapes calibration quantization step |

---

## Getting Started

For setup instructions, evaluation scripts, and custom configuration options, see the [unet on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/unet).

---

## Model Details

**Model Type:** Semantic segmentation (convolutional encoder-decoder)

**Base Model:** UNet-S5-D16 (mmsegmentation)

**Model Stats:**
- Input (`input`): `(1, 3, 512, 1024)` float32
- Output (`output`): `(1, 19, 512, 1024)` float32
- Precision tested: FP32 (CPU); FP32, FP16, BF16, INT8 (GPU)

---

## Accuracy Pipeline

Higher mIoU means predicted pixel labels agree more closely with ground truth across all 19 Cityscapes classes — 100% is perfect overlap, 0% is no agreement. Published paper mIoU for UNet-S5-D16 on Cityscapes is 69.10%.

### Metrics Explained

| Metric | Description |
|--------|-------------|
| mIoU | Mean Intersection-over-Union averaged across all 19 Cityscapes classes. Higher means better boundary alignment and class assignment. |

### Accuracy Results

**Cityscapes val mIoU** — UNet-S5-D16 (paper: 69.10%):

<!-- accuracy-table-start -->
| Device | Precision | mIoU |
|--------|-----------|------|
| CPU | FP32 | 69.31% |
| GPU | FP32 | 69.31% |
| GPU | FP16 | 69.32% |
| GPU | BF16 | 69.34% |
| GPU | INT8 | 69.31% |
<!-- 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/unet)**

The GitHub repository includes:
- Setup and prerequisites for ROCm environments
- Cityscapes val and UrbanSyn dataset staging scripts
- Full mIoU evaluation pipeline via mmsegmentation Runner
- Benchmarking and profiling scripts across CPU, GPU, and NPU