segformer

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

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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.

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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.

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Getting Started

For setup instructions, evaluation scripts, and custom configuration options, see the segformer on GitHub.

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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)

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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

Accuracy Results

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

<!-- accuracy-table-start --> <!-- accuracy-table-end -->

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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:

• 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