YOLOv11 - Instance Segmentation

YOLOv11 is the latest iteration in the YOLO series developed by Ultralytics. The instance segmentation variant provides state-of-the-art performance with pixel-precise masks for each detected object.

Overview

YOLOv11 for instance segmentation represents the cutting edge of real-time instance segmentation. Key features include:

• Enhanced architecture - Improved backbone and neck design for better feature extraction
• Pixel-precise masks - High-quality segmentation masks for each detected object
• Improved accuracy - Better performance than YOLOv8 on COCO dataset
• Efficient inference - Optimized for real-time applications
• 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 YOLOv11 models trained on or uploaded to the Roboflow platform for commercial use.
• Free Roboflow customers: Can use YOLOv11 via the serverless hosted API, or commercially self-hosted with a paid plan.

Learn more: Roboflow Licensing | YOLOv11 License Details

Pre-trained Model IDs

All pre-trained YOLOv11 instance segmentation models are trained on the COCO dataset (80 classes) and are open access (no API key required).

Model Size	640×640
Nano	yolov11n-seg-640
Small	yolov11s-seg-640
Medium	yolov11m-seg-640
Large	yolov11l-seg-640
Extra-Large	yolov11x-seg-640

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
trt	trt10

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

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

Usage Example

import cv2
import supervision as sv
from inference_models import AutoModel

# Load model and image
model = AutoModel.from_pretrained("yolov11n-seg-640")
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)