---

name: training description: "Routes BackgroundMattingV2 base and refine training, data layout, and benchmark setup workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

training

Use this sub-skill when the task is about configuring datasets, launching the training scripts, or validating the paired image layout used by the repo.

Typical triggers

• train_base.py
• train_refine.py
• data_path.py
• paired foreground / alpha / background data layout
• checkpoint, log, or TensorBoard output setup
• CUDA, NCCL, or multi-GPU assumptions
• the benchmark script in eval/

Read first

• references/workflows.md for the base/refine training flow and the data path shape.
• references/troubleshooting.md for dataset, CUDA, batch-size, and flag-name pitfalls.
• scripts/check_data_layout.py before editing data_path.py or launching a long run.
• scripts/run_training.py when you want a dry-run or execute wrapper around the checkout's training CLIs.

What this sub-skill owns

• training dataset configuration and validation
• data_path.py editing guidance
• checkpoint and log directory conventions
• base vs refine training behavior
• distributed GPU assumptions in refine training
• benchmark layout and metric expectations

What it does not own

• demo inference; use inference-and-demo
• TorchScript or ONNX export; use export-and-backends

Recommended first checks

1. Validate the data layout before touching the training loop.
2. Confirm the dataset name is one of the real foreground/alpha datasets.
3. Use the dry-run wrapper before any expensive launch.

Cross-links

• ../inference-and-demo/SKILL.md
• ../export-and-backends/SKILL.md
• ../../references/data-formats.md