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

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

# SegFormer: Optimized for AMD ROCm

SegFormer is a semantic segmentation model that assigns a class label to every pixel across 19 Cityscapes categories. 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 SegFormer found [here](https://github.com/NVlabs/SegFormer).
This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [segformer AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/segformer) 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, 19 classes)

**Output metrics:** mIoU (mean Intersection over Union)

> **Model:** SegFormer-B5 only at 1024×1024 — no `MODEL_SIZE` variants.

---

## 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/FP16/BF16/INT8), GPU (MIGraphX — FP32/FP16/BF16/INT8), and NPU (VitisAI — FP32/FP16/BF16/INT8).
- No code changes required versus the upstream SegFormer implementation — only environment/runtime configuration differs.
- NPU INT8 requires a separate Quark + Cityscapes calibration quantization step before evaluation.

| 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) | First run pays a 30+ minute graph-compilation cost |
| 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 [segformer on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/segformer).

---

## Model Details

**Model Type:** Semantic segmentation (Transformer-based)

**Base Model:** SegFormer-B5, 1024×1024 input resolution

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

---

## 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. Values above ~80% on Cityscapes val are considered strong for this task. Paper mIoU for SegFormer-B5 is 82.4%.

### Metrics Explained

| Metric | Description |
|--------|-------------|
| mIoU | Primary segmentation metric — mean Intersection-over-Union averaged across all 19 Cityscapes classes. Higher means better boundary alignment and class assignment across the full val set. |

### Accuracy Results

**Full Dataset Evaluation (Cityscapes val)** — SegFormer-B5:

<!-- accuracy-table-start -->
| Device | Precision | mIoU |
|--------|-----------|------|
| CPU | FP32 | 82.25% |
| GPU | FP32 | 82.25% |
| GPU | FP16 | 82.20% |
| GPU | BF16 | 82.26% |
| GPU | INT8 | 82.25% |
| NPU | FP32 | 82.25% |
| NPU | FP16 | 82.25% |
| NPU | BF16 | 82.23% |
| NPU | INT8 | 1.34% |
<!-- 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/segformer)**

The GitHub repository includes:
- Setup and prerequisites for ROCm environments
- Cityscapes val dataset staging and mIoU evaluation pipeline via mmsegmentation
- NPU INT8 quantization workflow (AMD Quark + Cityscapes calibration)
- Benchmarking and reproduction instructions across CPU, GPU, and NPU