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segmentation

segmentation

Use this YOLOv5 sub-skill for instance-segmentation training, validation, prediction, mask data, segmentation checkpoints, and mask troubleshooting.

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Skill 문서

GitHub

name: segmentation description: "Use this YOLOv5 sub-skill for instance-segmentation training, validation, prediction, mask data, segmentation checkpoints, and mask troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

YOLOv5 Segmentation

Use this route for YOLOv5 instance segmentation workflows involving segment/predict.py, segment/train.py, segment/val.py, *-seg.pt checkpoints, segmentation YAMLs, polygon labels, masks, or mask-specific failures.

Choose the workflow

  • Predict masks: read references/workflows.md for segmentation prediction command shapes, --retina-masks, output directories, and source handling. Use scripts/plan_segmentation_command.py before running commands.
  • Train masks: use the training section for coco128-seg.yaml, custom segmentation labels, pretrained vs scratch segmentation configs, DDP, and output planning.
  • Validate masks: use the validation section for mask metrics, --overlap, --mask-ratio, confidence/IoU, and exported-format validation handoff.
  • Data formats: read references/data-formats.md before converting detection labels or assuming a dataset is segmentation-ready.

Common decisions

  • Use segmentation checkpoints such as yolov5s-seg.pt; do not use plain detection checkpoints for mask workflows unless the task explicitly builds a new segmentation model.
  • Use models/segment/yolov5*-seg.yaml when training from scratch.
  • Treat full COCO segments as large; prefer coco128-seg.yaml or a tiny local fixture for smoke work.
  • Keep --project and --name explicit because prediction/training/validation write run artifacts.
  • Use CPU for parser/import checks and CUDA for realistic training or high-resolution prediction.

Handoffs

  • Read root references/datasets-and-weights.md for segmentation checkpoint names and dataset download risks.
  • Route detection-only boxes to ../detection/SKILL.md and classification to ../classification/SKILL.md.
  • Route export/deployment formats for a segmentation checkpoint to ../export/SKILL.md.
  • Route Flask serving to ../serving/SKILL.md only if the service itself is the user's task.
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