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Produktivität · SKILL.md

train-eval

train-eval

Routes AdelaiDet config selection, training, evaluation, Detectron2 launch flags, checkpoints, and model-family run workflows.

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Skill-Dokumentation

GitHub

name: "train-eval" description: "Routes AdelaiDet config selection, training, evaluation, Detectron2 launch flags, checkpoints, and model-family run workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

train-eval

Use this sub-skill when a task asks how to train, evaluate, resume, benchmark, debug a config, or launch an AdelaiDet model family through the repository's Detectron2-style training workflow.

Use this route for

  • Selecting a config family for FCOS, BlendMask, CondInst, BoxInst, SOLOv2, MEInst, FCPose, DenseCL, or non-text detection/segmentation tasks.
  • Building safe AdelaiDet training/evaluation commands with config overrides.
  • Running --eval-only, --resume, distributed launch flags, or checkpoint-backed evaluation.
  • Understanding output directories, dataset names, solver/batch overrides, and MODEL.WEIGHTS behavior.
  • Diagnosing model registry/config errors after installation is already healthy.

Do not use this route for

  • Install/build/import failures. Use ../setup-build/SKILL.md first.
  • Text spotting lexicons/dictionaries/evaluation. Use ../text-spotting/SKILL.md.
  • Dataset conversion or semantic mask generation. Use ../data-prep/SKILL.md.
  • Demo visualization only. Use ../demo-visualize/SKILL.md.
  • Checkpoint key conversion or ONNX export. Use ../export-convert/SKILL.md.

Read first

  • references/config-selection.md to choose a model/config family.
  • references/train-eval-workflows.md for launch patterns, flags, and outputs.
  • ../../references/model-overview.md for package-wide model routing.
  • ../../references/api-reference.md for verified config and registry surfaces.

Skill-owned scripts

  • scripts/run_train_eval.py — validates common paths and builds/runs an AdelaiDet training or evaluation command. Use --dry-run before expensive jobs.

Typical workflow

  1. Confirm setup:

    python ../../scripts/check_install.py --cuda-ops
    
  2. Choose the config family and config YAML.

  3. Confirm datasets are registered and available; switch to data-prep if not.

  4. Dry-run the launch:

    python scripts/run_train_eval.py --repo-root /path/to/AdelaiDet \
      --config configs/FCOS-Detection/R_50_1x.yaml --num-gpus 1 --dry-run
    
  5. Add --eval-only --model-weights /path/to/model.pth for evaluation-only runs, or --resume for continuation.

  6. Inspect OUTPUT_DIR, logs, metrics, checkpoints, and evaluator output.

Decision points

  • If the task names BAText/ABCNet or text recognition, load text-spotting before finalizing training flags.
  • If the config references generated semantic masks, PIC/LVIS/COCO conversions, or MEInst components, load data-prep before launch.
  • If checkpoint keys mismatch, switch to export-convert for conversion utilities.
  • If No object named ... in registry appears, verify setup/import first, then check config family/key spelling.
Gespeicherte Skills