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name: training-and-evaluation description: "Safely build and adapt pytorch-semseg training and validation workflows, including dry-run commands, checkpoints, logs, metrics, and costly prerequisite checks." disable-model-invocation: true metadata: disco-role: operating license: MIT

pytorch-semseg Training and Evaluation

Use this sub-skill when the task is to run, adapt, or explain the repository's training and validation workflows without reopening the original source files. The bundled scripts are dry-run command builders: they print commands and warnings, but never launch training, validation, downloads, dataset scans, or writes.

Start here

• Build a training command with scripts/build_train_command.py, then review references/workflows.md before running it manually.
• Build a validation command with scripts/build_validate_command.py, then review references/checkpoints-and-metrics.md for checkpoint and metric interpretation.
• For failures, use references/troubleshooting.md.

Covered capabilities

• Training entry point: python train.py --config CONFIG.
• Validation entry point: python validate.py --config CONFIG --model_path CHECKPOINT with --eval_flip / --no-eval_flip and --measure_time / --no-measure_time.
• Training loop wiring: seeds, augmentations, get_loader, get_model, DataLoader, runningScore, optimizer, scheduler, loss, TensorBoardX SummaryWriter, checkpoint save/resume.
• Validation loop wiring: checkpoint model_state, convert_state_dict, flip averaging, fps reporting, metric keys, and per-class IoU output.
• Safety decisions for dataset-bound or expensive runs.

Route elsewhere

• YAML schema design, dataset layouts, loader-specific split names, and config validation belong to the sibling data-and-configs sub-skill.
• Model constructor parameters, loss/optimizer/scheduler registry tables, and API-level model selection belong to model-zoo-and-apis.
• test.py single-image inference, output masks, palettes, and DenseCRF belong to single-image-inference.

Safe workflow pattern

1. Use the relevant command builder in this directory.
2. Treat every warning as a pre-run checklist item.
3. Resolve dataset/checkpoint/config compatibility issues using the routed sibling sub-skills when needed.
4. Only after explicit user approval for compute, dataset reads, and log/checkpoint writes should a future agent run the printed command manually from an appropriate repository checkout.