# YOLOv8-seg Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/yolov8-seg
> Markdown URL: https://aitinkerers.org/technologies/yolov8-seg.md
> Technology record last updated: 2026-02-26T19:58:15Z
> Generated: 2026-09-21T22:51:00Z

Ultralytics YOLOv8-seg provides high-speed instance segmentation by pinpointing object boundaries with pixel-level precision.

This model architecture from Ultralytics executes simultaneous object detection and mask generation for 80 COCO classes. The YOLOv8n-seg variant delivers 30.5 mAP-mask at 1.1ms inference speeds (A100 TensorRT), balancing accuracy with extreme throughput. It serves critical roles in autonomous navigation (obstacle avoidance), agriculture (crop health monitoring), and robotics (pick-and-place operations). Users can export weights to ONNX or OpenVINO for deployment on diverse hardware: from cloud GPUs to mobile CPUs.

- Official technology site: https://docs.ultralytics.com/tasks/segment/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Exercise Posture Tracking Using Computer Vision](https://raleigh.aitinkerers.org/talks/rsvp_QACg5zj28MM)

I've created an unsupervised computer vision algorithm to detect back rounding during deadlift. In short, I've demonstrated that I can measure the curvature of the back in an unsupervised way, and that a rounded back produces a different signal from a flat back. I've used off-the-shelf pose tracking and segmentation models to measure the back curvature. This could help guide learning of proper form during deadlift and other exercises.

- Event context: AI Tinkerers Raleigh Meetup — February 11, 2026 — 2026-02-11 — Raleigh
- Public talk page: https://raleigh.aitinkerers.org/talks/rsvp_QACg5zj28MM

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