---

name: data-and-training description: "Guide dataset layout, batch preprocessing outputs, ImageFolder loader semantics, GAN training CLI/options, checkpoint/resume behavior, and validation before training." disable-model-invocation: true metadata: disco-role: operating license: MIT

Data and Training

Use this sub-skill when you need to prepare Photo2Cartoon training data, validate the dataset/photo2cartoon layout, understand preprocessing outputs, or review the GAN training launch and checkpoint flow.

Route away from this sub-skill when the main topic is:

• generator/discriminator architecture or tensor internals: ../model-internals/SKILL.md
• face alignment, segmentation, or crop math: ../preprocessing/SKILL.md
• .pt / .onnx inference assets or prediction flow: ../portrait-inference/SKILL.md

Start Here

1. Read references/data-formats.md for the dataset tree, supported image extensions, ImageFolder semantics, and preprocessing output naming.
2. Read references/training-workflow.md for the train.py CLI, defaults, result-directory naming, checkpoint keys, and resume behavior.
3. Read references/troubleshooting.md when a split is missing, a folder is empty, an extension is ignored, a checkpoint load fails, or GPU memory is tight.
4. Run the bundled validator before training:

python scripts/validate_dataset_layout.py --dataset-root /path/to/dataset/photo2cartoon

5. Build the guarded training command from this skill instead of copying a source-script command by hand:

python scripts/build_training_command.py --repo-root /path/to/photo2cartoon-checkout --dataset photo2cartoon

Use --strict when unsupported files should fail the dataset check and --check-images when Pillow decode validation is useful.

Covered Facts

• dataset/photo2cartoon/{trainA,trainB,testA,testB} layout
• allowed image suffixes and recursive ImageFolder scanning
• preprocessing output naming and white-background face crops
• train.py defaults, result-directory naming, and checkpoint keys
• --resume / --pretrained_weights expectations
• loss weights, GPU limits, and batch-size cautions

Not Covered

• generator/discriminator internals
• face detection / segmentation implementation details
• inference recipes for model assets
• network downloads, training runs, or file writes outside the bundled validator

Bundled Helper

• scripts/validate_dataset_layout.py: safe, argparse-based dataset layout validator with explicit --dataset-root and optional stricter checks.
• scripts/build_training_command.py: guarded command builder for source-compatible train/test launches; dry-run by default and executes only with --execute.