---

name: configuration-and-data description: "Route BiRefNet configuration, task selection, and dataset layout checks." disable-model-invocation: true metadata: disco-role: operating license: MIT

configuration-and-data

Use this sub-skill when you need to inspect or adapt Config, choose task/testset/backbone/loss settings, or prepare a BiRefNet-style dataset tree before training or evaluation.

Handles

• Config() defaults and mutable knobs
• Built-in task names: DIS5K, COD, HRSOD, General, General-2K, Matting
• Data layout under <data-root>/<task>/<dataset>/{im,gt}
• MyData(...) pairing, augmentation, dynamic-size collation, and background_color_synthesis
• Auxiliary classification filename assumptions
• Safe dataset-tree validation for image/label pairing

Use the bundled scripts

• scripts/birefnet_config_summary.py — print distilled config defaults, task profiles, backbone choices, and optional checkout-inspected schedule notes.
• scripts/birefnet_dataset_check.py — validate a BiRefNet dataset tree, pair im/gt files, and report missing or duplicate basenames.

Read next

• references/configuration.md for task profiles, default values, path knobs, precision/compile, losses, and schedule extraction.
• references/data-formats.md for the exact dataset tree, image/label pairing rules, dynamic-size collation, and auxiliary classification filenames.
• references/troubleshooting.md when data folders are missing, counts differ, Config behaves differently outside a checkout, or dynamic-size training is unstable.

Recommended operating flow

1. Identify the task family (DIS5K, COD, HRSOD, General, General-2K, or Matting) and decide whether the user is changing data, model shape, or only paths.
2. Run scripts/birefnet_config_summary.py to confirm the distilled defaults; add --repo-root only when the user has a current checkout and wants live schedule inspection.
3. Validate each dataset with scripts/birefnet_dataset_check.py --data-root <root> --task <task> --dataset <name> before recommending training or evaluation.
4. If the user changes config.bb, size, dynamic_size, lambdas_pix_last, mixed_precision, or compile, route to the sibling workflow that depends on that field before finalizing a command.
5. Keep config edits explicit and reversible; BiRefNet stores many active choices directly in config.py, so hidden edits can affect model loading, training, and evaluation together.

Done criteria

• The data tree has matching im and gt basenames for every selected dataset.
• The selected task/testset/training-set names match the user's directory names.
• Any dynamic-size, load_all, auxiliary classification, or background synthesis assumptions are recorded before training begins.
• Architecture-sensitive config changes have been checked with model-architecture.

Route out

• Model internals and patch helpers -> ../model-architecture/SKILL.md
• Full training and evaluation commands -> ../training-and-evaluation/SKILL.md
• Image/video outputs and refinement -> ../inference-and-postprocessing/SKILL.md