YOLOv8 - Instance Segmentation

YOLOv8 is a popular computer vision model developed by Ultralytics. The instance segmentation variant extends object detection capabilities by providing pixel-precise masks for each detected object.

Overview

YOLOv8 for instance segmentation combines object detection with pixel-level segmentation masks. Key features include:

• Anchor-free detection head - Simplified architecture without anchor boxes
• Pixel-precise masks - Detailed segmentation for each detected object
• Improved feature pyramid network - Better multi-scale feature fusion
• Efficient backbone - Optimized for speed and accuracy
• 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 YOLOv8 models trained on or uploaded to the Roboflow platform for commercial use.
• Free Roboflow customers: Can use YOLOv8 via the serverless hosted API, or commercially self-hosted with a paid plan.

Learn more: Roboflow Licensing | YOLOv8 License Details

Pre-trained Model IDs

All pre-trained YOLOv8 instance segmentation models are trained on the COCO dataset (80 classes) and are open access (no API key required).

Model Size	640×640	1280×1280
Nano	yolov8n-seg-640	yolov8n-seg-1280
Small	yolov8s-seg-640	yolov8s-seg-1280
Medium	yolov8m-seg-640	yolov8m-seg-1280
Large	yolov8l-seg-640	yolov8l-seg-1280
Extra-Large	yolov8x-seg-640	yolov8x-seg-1280

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("yolov8n-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)