YOLO26 - Instance Segmentation

YOLO26 is the latest addition to the Ultralytics YOLO model series. The instance segmentation variant extends object detection capabilities by providing pixel-precise masks for each detected object.

Overview

YOLO26 for instance segmentation combines NMS-free object detection with pixel-level segmentation masks. Key features include:

• NMS-free end-to-end inference - Removing non-maximum suppression helps achieve lower inference latencies.
• DFL removal - Distribution Focal Loss removed for simpler export and broader edge compatibility
• Semantic segmentation loss - Improves model convergence
• Upgraded proto module - Uses multi-scale information to produce higher-quality masks
• Multiple model sizes - From nano to extra-large variants

License

AGPL-3.0

Commercial Licensing

• AGPL-3.0: Free for open-source projects. Requires derivative works to be open-sourced.
• Paid Roboflow customers: Automatically get access to use any YOLO26 models trained on or uploaded to the Roboflow platform for commercial use.
• Free Roboflow customers: Can use YOLO26 via the serverless hosted API, or commercially self-hosted with a paid plan.

Learn more: Roboflow Licensing | YOLO26 License Details

Pre-trained Model IDs

All pre-trained YOLO26 instance segmentation models are trained on the COCO dataset (80 classes) and are open access (no API key required).

Model Size	640×640
Nano	yolo26n-seg
Small	yolo26s-seg
Medium	yolo26m-seg
Large	yolo26l-seg
Extra-Large	yolo26x-seg

Custom model ID format: project-url/version (e.g., my-project-abc123/2)

Supported Backends

Backend	Extras Required
onnx	onnx-cpu, onnx-cu12, onnx-cu118, onnx-jp6-cu126
torch-script	torch-cpu, torch-cu118, torch-cu124, torch-cu126, torch-cu128, torch-jp6-cu126

Roboflow Platform Compatibility

Feature	Supported
Training	✅ Train custom models on Roboflow
Upload Weights	✅ Upload pre-trained weights
Serverless API (v2)	✅ Deploy via hosted API
Workflows	✅ Use in Workflows via Instance Segmentation block
Edge Deployment (Jetson)	✅ Deploy on NVIDIA Jetson devices
Self-Hosting	✅ Deploy with inference-models

Usage Example

import cv2
import supervision as sv
from inference_models import AutoModel

# Load your custom model (requires Roboflow API key)
model = AutoModel.from_pretrained(
    "my-project-abc123/2",
    api_key="your_roboflow_api_key"
)
image = cv2.imread("path/to/image.jpg")

# Run inference
predictions = model(image)
detections = predictions[0].to_supervision()

# Annotate image with masks
mask_annotator = sv.MaskAnnotator()
annotated_image = mask_annotator.annotate(image.copy(), detections)

# Save or display
cv2.imwrite("annotated.jpg", annotated_image)