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.
Task Overview
Task: Instance segmentation
Dataset: COCO val2017 (5,000 images, 80 categories)
Output metrics: Mask mAP@0.5:0.95, Mask mAP@0.5, Mask mAP@0.75, 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.
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.
| Runtime | Precision | Backend | Hardware | Notes |
|---|
| ONNX Runtime | FP32 | CPU Execution Provider | AMD CPU | — |
| PyTorch | FP32 | ROCm (HIP) | AMD Instinct™ / Radeon™ GPU | MIGraphX does not support segment ONNX ops |
| ONNX Runtime | BF16 | VitisAI Execution Provider | AMD Ryzen AI NPU | Auto-quantized internally; first run compiles in 5–10 minutes |
Getting Started
For setup instructions, evaluation scripts, and custom configuration options, see the
yolo26_segment on GitHub.
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)
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 mAP@0.5:0.95 are considered strong for COCO-scale instance segmentation.
Metrics Explained
| Metric | Description |
|---|
| Mask mAP@0.5:0.95 | Primary COCO mask metric — mean AP averaged across IoU thresholds 0.5–0.95. The strictest, most holistic segmentation accuracy number; higher means masks are both correctly classified and tightly overlap ground-truth at a range of overlap thresholds. |
| Mask mAP@0.5 | Mask AP at a single, looser IoU threshold of 0.5 — a prediction only needs 50% pixel overlap with the ground-truth mask to count as correct, so this is typically higher than mAP@0.5:0.95 and reflects "did it find the object" more than "how precise is the mask boundary." |
| Mask mAP@0.75 | Mask AP at a stricter IoU threshold of 0.75 — the predicted mask must overlap ground truth by 75%, rewarding precise boundary delineation, not just correct detection. |
| Bbox mAP | Bounding-box mAP@0.5:0.95 — measures detection quality (box localization) independently of mask quality, useful for isolating whether errors come from detection or segmentation. |
| Mask Precision | Of all mask predictions the model produced, what fraction were correct — high precision means few false positive masks. |
| Mask Recall | Of all ground-truth objects, what fraction did the model find within its top 100 detections per image — high recall means few missed objects. |
| Mask F1 | Harmonic mean of mask precision and recall — balances the tradeoff between finding all objects and avoiding false detections. |
| Mask mAP-small/medium/large | Mask mAP@0.5:0.95 broken down by object size — small objects are typically the hardest to segment, exposing size-specific weaknesses a single aggregate score would hide. |
Accuracy Results
Full Dataset Evaluation (COCO val2017) — MODEL_SIZE=n:
| Device | Precision | Mask mAP@0.5:0.95 | Mask mAP@0.5 | Mask mAP@0.75 | Bbox mAP | Mask Precision | Mask Recall |
|---|
| CPU | FP32 | 0.3340 | 0.5258 | 0.3528 | 0.3962 | 0.3031 | 0.4244 |
| GPU | FP32 | 0.2757 | 0.4774 | 0.2777 | 0.3954 | 0.2534 | 0.3617 |
| NPU | BF16 | 0.0008 | 0.0022 | 0.0005 | 0.0041 | 0.0007 | 0.0020 |
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.
Dig Deeper
Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples?
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
