model-scaffold

Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, image-to-image synthesis, self-supervised pretraining, or fine-tuning a pretrained backbone (transfer learning) — the missing middle link between choosing an architecture and validating a trained model. Emits a patient-level seed-locked split as an auditable artifact, a task-appropriate model, train and evaluate scripts that seed every RNG and infer under eval mode, a config, requirements, a reproducibility record, and a Methods stub with VERIFY placeholders (no fabricated numbers). Fine-tuning mode adds a frozen-then-unfrozen schedule, discriminative learning rates, and a pretrained-weight provenance record. The reproducibility guarantees hold by construction, so the build is leakage-safe before any training runs. Integrates with MONAI, nnU-Net, TorchIO, timm, and torchvision — it does not reimplement them.