yolo26_detect

YOLO26: Optimized for AMD ROCm

YOLO26 is a real-time object detection model that detects and localizes axis-aligned bounding boxes across 80 COCO categories in a single forward pass. This repository packages inference for object detection 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 YOLO26 found here. This repository contains configurations and scripts optimized for AMD® ROCm™ platforms. You can use the yolo26_detect 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: Object detection

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

Output metrics: [email protected]:0.95, [email protected], [email protected], AR@100, per-size mAP/AR (small/medium/large)

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

GPU/NPU note: MIGraphX runtime-quantizes a single FP32 ONNX model to cover all GPU precisions. NPU execution requires BF16 — VitisAI quantizes the FP32 ONNX internally at runtime using configs/vitisai/detect_bf16.json.

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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), GPU (MIGraphX Execution Provider — FP32/FP16/BF16/INT8), and NPU (VitisAI Execution Provider — BF16, auto-quantized internally).
• No code changes required versus the upstream Ultralytics YOLO26 implementation — only environment/runtime configuration differs.
• GPU first run: MIGraphX compiles the model on first use (30+ minutes); compiled kernels are cached and reused afterwards.

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Getting Started

For setup instructions, evaluation scripts, and custom configuration options, see the yolo26_detect on GitHub.

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Model Details

Model Type: Object detection (single-pass CNN detector)

Base Model: YOLO26 (Ultralytics), size n default

Model Stats:

• Input: (1, 3, 640, 640) float32
• Output: (1, 300, 6) float32
• Precision tested: FP32 (CPU); FP32, FP16, BF16, INT8 (GPU); BF16 (NPU)

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Accuracy Pipeline

COCO quality evaluation is fully implemented — make eval-<device> runs inference across all 5,000 COCO val2017 images and scores predictions with pycocotools, the reference implementation for axis-aligned boxes.

Higher mAP means the model's predicted boxes and classes agree more closely with ground truth across the dataset — 1.0 is perfect detection, 0.0 means no correct detections. In practice, values above ~0.5 for [email protected]:0.95 are considered strong for COCO-scale object detection.

Metrics Explained

Accuracy Results

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

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

NPU accuracy anomaly: the NPU BF16 run above reports near-zero mAP despite running genuinely on-device (execution_provider: VitisAIExecutionProvider, device_fell_back_to_cpu: False — not a silent CPU fallback). This is well outside the normal BF16-quantization delta and looks like a broken NPU accuracy path (e.g. a postprocessing/decode mismatch specific to the NPU export) rather than expected precision loss, and needs investigation before treating this number as representative.

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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