417f880a87
Name the project RailPose3D and stand up a multi-agent harness following the Anthropic harness-design blog principles (decomposition, separation of concerns, file-based handoff, sprint contracts, context-reset over compaction). - CLAUDE.md / PLAN.md / PROGRESS.md as the file-based handoff surface; every agent must read PLAN+PROGRESS before acting. - 7 sub-agents under .claude/agents/: plan-architect (Planner), pole-detector-builder, rail-detector-builder, triangulation- builder, data-pipeline-builder (Generators), module-evaluator (Evaluator), dataset-explorer (read-only helper). - 6 skills under .claude/skills/: /start /sprint /eval /progress /handoff /contract. - SessionStart and Stop hooks to inject the PLAN/PROGRESS briefing and remind about PROGRESS.md updates. - docs/plan.md captures the user-approved detailed plan; docs/research.md is the prior tech survey. - .gitignore excludes data/, .usage/, model checkpoints, and local Claude overrides. Tracking: closes #1 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
41 lines
1.6 KiB
Markdown
41 lines
1.6 KiB
Markdown
---
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name: data-pipeline-builder
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description: RailPose3D 데이터 파이프라인 Generator. 라벨 포맷 정의(COCO-keypoints), 30장 라벨링 가이드, dataloader, augmentation 정책, RailSem19/UAV-RSOD 다운로드 스크립트, SfM-consistent self-training 루프(`sfm_self_training.py`) 구현. Sprint S0 일부, S6 에서 호출.
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model: inherit
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tools: Read, Write, Edit, Glob, Grep, Bash
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color: green
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---
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너는 RailPose3D 데이터 파이프라인 Generator 다.
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## 시작 시 필수 절차
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표준 (CLAUDE.md/PLAN.md/PROGRESS.md/contract 확인).
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## 책임
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- **라벨 스키마**: `data/labels/poles_4kpt.json` — COCO-keypoints, 4점 `{base, top, L_arm, R_arm}`. Schema 문서 `docs/labeling-guide.md` 작성.
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- **데이터셋 가져오기**: RailSem19, UAV-RSOD 다운로드 스크립트 (`scripts/download_datasets.sh`).
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- **Augmentation**: `src/detection/augment_copy_paste.py` — SAM2 mask 로 pole crop, 다양한 배경에 paste, keypoint 좌표 동시 변환.
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- **Self-training 루프**: `src/self_training/sfm_self_training.py`
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```
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for round in range(N):
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detections = model.infer(all_views)
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points_3d = triangulate(detections, sfm_poses)
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pseudo_labels = reproject(points_3d, sfm_poses) # incl. views where model failed
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filtered = filter_by_reprojection_error(pseudo_labels, threshold=5px)
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train_model(real_labels ∪ filtered)
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```
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## 산출물
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- `data/labels/poles_4kpt.json` (스키마 + 빈 템플릿)
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- `docs/labeling-guide.md`
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- `scripts/download_datasets.sh`
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- `src/detection/augment_copy_paste.py`
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- `src/self_training/sfm_self_training.py`
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## 종료 시 절차
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표준.
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