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name: training description: "Guides CUDA-only SiamMask base/refine and SiamRPN training, checkpoint resume, experiment configs, and training setup validation." disable-model-invocation: true metadata: disco-role: operating license: MIT

SiamMask Training

Use this sub-skill when the task is to train or resume SiamMask/SiamRPN models, inspect experiment configs, plan data requirements, or debug CUDA/batch/checkpoint issues.

Hard Requirement

The native training scripts call CUDA APIs unconditionally (model.cuda() and DataParallel(...).cuda()). Treat CUDA as required with no CPU substitute for real training. CPU-only work can validate configs and commands, but it does not verify training runtime.

Prerequisites

• Read root install/setup and model overview.
• Run the root environment probe with --expect-cuda yes before real training.
• Prepare training data through ../data-preparation/SKILL.md. The configs expect crop511 directories and JSON indexes.
• Obtain resnet.model or an appropriate base checkpoint before training from scratch/refine.

Main Routes

User intent	What to do
Train SiamMask base	Read references/workflows.md and use scripts/run_training.py base in dry-run mode first.
Train the refine/sharp model	Read references/workflows.md; provide --pretrained <base-checkpoint>.
Train the SiamRPN baseline	Read references/workflows.md and use scripts/run_training.py siamrpn.
Inspect config/data paths	Read references/config-reference.md; the bundled runner summarizes dataset roots and JSON paths before launching.
Debug training failure	Read references/troubleshooting.md.

Bundled Helper

Use scripts/run_training.py to compose commands and inspect config paths. It:

• Defaults to dry-run and prints the command, cwd, PYTHONPATH, and config dataset summary.
• Sets PYTHONPATH to include checkout root and experiment directory.
• Supports base, refine, and siamrpn modes.
• Lets you set --gpu <CUDA_VISIBLE_DEVICES> before execution.
• Only starts training when --run is supplied.

Example dry-run:

python scripts/run_training.py --repo-root <siammask-checkout> --gpu 0,1,2,3 base \
  --config config.json --batch 64 --workers 20 --epochs 20

Add --run before base only after the user approves a long CUDA training job.

Do Not

• Do not claim training is verified from CPU imports or command help.
• Do not start full training without checking disk space, dataset availability, checkpoint paths, and user approval.
• Do not reuse refine checkpoints with base-only configs or box-only SiamRPN flags.
• Do not hide missing dataset roots; training scripts will otherwise fail after allocating GPU/data-loader resources.