yolo26_segment

YOLO26-seg: Optimized for AMD ROCm

YOLO26 is a real-time instance segmentation model that detects objects and produces pixel-level masks across 80 COCO categories in a single forward pass. This repository packages inference for instance segmentation using ONNX Runtime (CPU, NPU) and PyTorch (GPU), exported and validated for AMD ROCm so it runs efficiently on AMD GPUs, CPUs, and NPUs.

This is based on the implementation of YOLO26 found here. This repository contains configurations and scripts optimized for AMD® ROCm™ platforms. You can use the yolo26_segment 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: Instance segmentation

Dataset: COCO val2017 (5,000 images, 80 categories)

Output metrics: Mask [email protected]:0.95, Mask [email protected], Mask [email protected], Bbox mAP, Mask Precision, Mask Recall, Mask F1, per-size Mask mAP (small/medium/large)

Model variants: Default is n. Override with MODEL_SIZE=n/s/m/l/x.

GPU note: GPU quality uses PyTorch .pt models directly — MIGraphX does not support YOLO segment ONNX ops, so GPU runs FP32 only via PyTorch rather than MIGraphX.

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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 on CPU (FP32) and NPU (VitisAI — auto-quantized internally, BF16), and PyTorch/ROCm on GPU (FP32 only).
• No code changes required versus the upstream Ultralytics YOLO26-seg implementation — only environment/runtime configuration differs.
• First NPU run takes 5–10 minutes for model compilation; subsequent runs use the cached compiled model.

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Getting Started

For setup instructions, evaluation scripts, and custom configuration options, see the yolo26_segment on GitHub.

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Model Details

Model Type: Instance segmentation (single-pass CNN detector with mask-proto head)

Base Model: YOLO26-seg (Ultralytics), size n default

Model Stats:

• Input: (1, 3, 640, 640) float32
• Output (detections): (1, 300, 38) float32
• Output (mask protos): (1, 32, 160, 160) float32
• Precision tested: FP32 (CPU, GPU); BF16 (NPU)

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Accuracy Pipeline

COCO instance segmentation quality evaluation is fully implemented — make eval-fulldataset-<device> runs inference across all 5,000 COCO val2017 images and computes official COCO metrics via pycocotools.

Higher mask mAP means the model's predicted segmentation masks agree more closely with ground-truth masks — 1.0 would be perfect pixel-level segmentation, 0.0 means no correct predictions. In practice, values above ~0.35 for mask [email protected]:0.95 are considered strong for COCO-scale instance segmentation.

Metrics Explained

Accuracy Results

Full Dataset Evaluation (COCO val2017) — MODEL_SIZE=n:

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

Note: GPU quality uses PyTorch .pt models directly (MIGraphX does not support segment ONNX ops). CPU mask mAP is higher than GPU because the ONNX post-NMS postprocessing applies tighter bbox-cropped masks. NPU quality is severely degraded by VitisAI's internal BF16 quantization — the model produces very few detections (bbox mAP drops from ~0.40 to 0.004), making NPU unsuitable for accuracy-critical workloads with this model.

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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
• Full COCO val2017 evaluation pipeline via pycocotools
• Per-operator latency profiling scripts (including NPU AI Analyzer integration)
• Benchmarking and reproduction instructions across CPU, GPU, and NPU