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YOLOv8-Segmentation

Real-time object segmentation optimized for mobile and edge by Ultralytics.

Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.

Model RepositoryHugging FaceResearch Paper

Technical Details

Model checkpoint:YOLOv8N-Seg
Input resolution:640x640
Number of parameters:3.43M
Model size:13.2 MB
Number of output classes:80

Applicable Scenarios

  • Factory Automation
  • Robotic Navigation
  • Camera

Licenses

Source Model:AGPL-3.0
Deployable Model:AGPL-3.0

Tags

  • real-time
    A “real-time” model can typically achieve 5-60 predictions per second. This translates to latency ranging up to 200 ms per prediction.

Supported Compute Chipsets

  • Snapdragon® X Elite