---

name: training-and-evaluation description: "Route BiRefNet training launches, resume semantics, evaluation metrics, and best-checkpoint selection." disable-model-invocation: true metadata: disco-role: operating license: MIT

training-and-evaluation

Use this sub-skill for BiRefNet fine-tuning, epoch scheduling, checkpoint loading/saving, metric evaluation, and best-epoch selection.

Handles

• train.py CLI: --resume, --epochs, --ckpt_dir, --dist, --use_accelerate
• train.sh task-specific epoch, validation, and save schedules
• train_test.sh train-then-evaluate orchestration
• test.sh inference plus evaluation over configured testsets
• eval_existingOnes.py, evaluation.metrics.evaluator, sort_and_round_scores
• gen_best_ep.py checkpoint ranking from e_results/*_eval.txt

Use the bundled script

• scripts/birefnet_metric_smoke.py — create tiny mask fixtures and exercise evaluation.metrics.evaluator from an explicit or inferred repo root.

Read next

• references/training-workflows.md
• references/evaluation-workflows.md
• references/metrics-reference.md
• references/troubleshooting.md

Recommended operating flow

1. Validate dataset layout and task/testset names with ../configuration-and-data/SKILL.md before building a training or evaluation command.
2. Confirm backbone/weight compatibility with ../model-architecture/SKILL.md; wrong config.bb often appears as a checkpoint tensor-size error.
3. For fine-tuning, compute total epochs from the checkpoint filename epoch plus the desired additional epochs because train.py resumes at filename epoch + 1.
4. Treat full training as GPU/data/weight/budget dependent; use this sub-skill to plan commands and only execute after explicit user approval.
5. Before evaluating real predictions, run scripts/birefnet_metric_smoke.py to confirm metric dependencies and evaluator wiring.

Done criteria

• Training commands name the run directory, GPU IDs, total epoch count, resume path, and whether Accelerate/DDP is used.
• Evaluation commands have matching gt_root, pred_root, dataset names, and metric flags.
• Checkpoint selection criteria (sm, wfm, and DIS-only hce) are explicit.
• Asset-heavy native runs are marked skipped unless the user provides data, weights, hardware, and time budget.

Route out

• Dataset tree and task/testset selection -> ../configuration-and-data/SKILL.md
• Backbone choice, checkpoint key cleanup, and weight compatibility -> ../model-architecture/SKILL.md
• Standalone image/video mask generation and foreground refinement -> ../inference-and-postprocessing/SKILL.md