feat(model-lane): v5.0 Phase 5 — build-lane breadth (model-scaffold 5 tasks + architecture-zoo detection/synthesis)#224
Merged
Conversation
… tasks + architecture-zoo detection/synthesis) Expands the existing /model-scaffold and /architecture-zoo skills; NO new skills/ detectors/probes (counts unchanged: 51 skills / 41 detectors / 38 guidelines), torch-free CI. - /model-scaffold now generates 5 task types (was segmentation-only): --task segmentation (U-Net) | classification (small multi-label CNN; swap in timm) | detection (torchvision Faster R-CNN + FPN) | synthesis (Pix2Pix U-Net generator + PatchGAN) | ssl (SimCLR encoder + projection head, NT-Xent). Refactored scaffold.py into a TASKS registry; the patient-level seed-locked split is task-independent, and each emitted train.py/evaluate.py is hygienic by construction (all RNGs seeded via a shared seed_everything, cuDNN deterministic, train-only loader, eval()+no_grad()). The challenge + regression test now verify ALL 5 tasks (frozen split + valid Python + check_training_hygiene, network-free; segmentation keeps the torch forward tier). - /architecture-zoo adds detection.md (R-CNN family / Faster R-CNN+FPN / Mask R-CNN / RetinaNet / YOLO / DETR) + synthesis.md (Pix2Pix / CycleGAN / SPADE / diffusion / VAE / fastMRI) family cards, each grounded in its source paper with when-to-use, medical use, reference impl, validation setup, and matching scaffold template; the index decision tree now routes to them (no more "forthcoming"). Package size unchanged in practice (templates are small .py/.md text — no weights; npm audit passes). All CI-mirror gates green locally. Version stays 4.11.0 — this lands in [Unreleased] for the next release / v5.0.0. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Phase 5 of the v5.0 model-engineering lane — build-lane breadth. Expands the existing
/model-scaffoldand/architecture-zooskills; no new skills / detectors / probes (counts unchanged — 51 skills / 41 detectors / 38 guidelines), torch-free CI. Lands in[Unreleased]for the next release / v5.0.0.What's in this PR
/model-scaffoldnow generates 5 task types (was segmentation-only):--tasksegmentation (U-Net), classification (small multi-label CNN; swap in atimmbackbone), detection (torchvision Faster R-CNN + FPN), synthesis (Pix2Pix U-Net generator + PatchGAN), ssl (SimCLR encoder + projection head, NT-Xent). Refactoredscaffold.pyinto aTASKSregistry. The patient-level seed-locked split is task-independent, and each emittedtrain.py/evaluate.pyis hygienic by construction (all RNGs seeded via a sharedseed_everything, cuDNN deterministic, train-only loader,eval()+no_grad()). The challenge + regression test now verify all 5 tasks (frozen split + valid Python +check_training_hygiene, network-free; segmentation keeps the torch forward tier)./architecture-zooaddsdetection.md+synthesis.mdfamily cards — R-CNN family / Faster R-CNN+FPN / Mask R-CNN / RetinaNet / YOLO / DETR; Pix2Pix / CycleGAN / SPADE / diffusion / VAE / fastMRI — each grounded in its source paper with when-to-use, medical use, reference implementation, validation setup, and the matching scaffold template. The decision-tree index now routes to them (no more "forthcoming").Notes
.py/.mdtext — no model weights; the npm tarball audit passes and size is unchanged in practice (~19 MB, dominated by pre-existing journal profiles / checklists).Verification
All CI-mirror gates pass locally:
validate_skills.sh, everygen_*.py --check,validate_catalog_consistency,check_domain_probe_sync --strict, frontmatter / routing-assets / locale / version / npm-audit, plus the updated multi-task challenge + regression test.🤖 Generated with Claude Code