Get startedModels · Local training
Train a model to outline objects
Segmentation learns object shapes. Training your own model is separate from using the built-in segmentation service.
Source reviewed 2026-09-16. Availability depends on your installation, permissions, and compatible runtimes. A supported path is not a guarantee of model quality or production readiness.
Outcome
A local YOLO11 Nano segmentation model with an ONNX mask artifact, if training and export checks pass.
Start here
What you need to know first
Check the inputs below before starting. If you are new, begin with the first-project guide. A dataset holds media and labels; a model produces results; a deployment makes a selected model version callable. Creating one does not create the others.
Bring these inputs
- — Confirmed polygon annotations
- — Training and validation splits
- — Local training runtime
Expected output and limits
- — A checkpoint and mask ONNX artifact if successful
- — Training report and dataset lineage
Core workflow
Select reviewed outlines
Choose a segmentation dataset version with confirmed simple polygon instances. Boxes alone, holes, disconnected shapes, and self-crossing polygons are not supported training annotations.
Check the split
Prepare training split if training or validation is missing. The new version retains test images and review status. Generated data must retain its complete brief, unique images, scenario coverage, and confirmed annotations before training.
Train on This machine
Use the local supervised path with one, two, or three configurations. The total epoch ceiling is configurations × epochs. Score keeps each validation score and both artifacts, then selects the strongest mask score. Remote segmentation and architecture search are not enabled.
Test the saved model
Inspect actual masks on separate reviewed images. An exported model and a small local smoke test do not establish customer accuracy.
What this surface supports
Local supervised segmentation and bounded configuration search
Reviewed polygon validation
Checkpoint and ONNX export
Mask-based evaluation
Expert section
Contracts, signals, and failure modes
Use this section when you are defining acceptance criteria, automating the surface, or reviewing whether its output is safe to promote downstream.
Quality and operating signals
- — Actual exported masks
- — Independent mask AP and missed objects
Common failure modes
- — Invalid polygon topology
- — Unreviewed or overlapping data
- — Remote target selected for a local-only path
Expert release checklist
□ Inputs and dependencies are pinned to immutable versions.
□ Acceptance metrics include critical classes and operating slices.
□ Failure, retry, cost, and rollback behavior are understood.
□ The resulting artifact has an owner and a downstream review path.
Engineering safeguards
- — Built-in segmentation weights remain protected
- — A dataset task must match the trainer
- — Independent quality testing is still required