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Add annotated image output + 7 beginner notebooks
ccdp.viz.overlay renders YOLO detector boxes onto a PIL image (per-class color palette, Pillow-only, 8 unit tests). Wired into: - Gradio demo: new Image component shows boxes alongside the JSON panel - FastAPI: new /estimate/annotated endpoint returns PNG bytes Beginner notebook series under notebooks/ (00-06) walks through every model from first principles: convolution math, residual connections, two-stage transfer learning, IoU/NMS/mAP, gradient boosting, the catalog calibration trick, end-to-end inference. Each notebook has a Colab bootstrap, demo-scale runnable training cells, and an opt-in full-training cell. Generated via notebooks/_build_notebooks.py (committed so future tweaks edit Python, not ipynb JSON). Tests: 84/84 passing (76 existing + 8 new). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>5b27429
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checkpoint-3: comparison report, FastAPI, Gradio, HF Space deploy
Phase 3 brings the system to humans. Three deliverables, all reading from the existing v0.1.0 production registry — no new training, no model changes. ## New modules ccdp/preprocess (Stage A: deterministic downscale + quality report). - `normalize_for_inference` LANCZOS-resizes long edges to 1600 px max. - `quality_report` returns sharpness (Laplacian variance), brightness, contrast, low-resolution / low-light / blurry booleans. - Stage B (super-resolution) explicitly deferred to a later checkpoint. ccdp/eval — Variant A vs B head-to-head evaluator. - `metrics.py`: pure per-class P/R/F1 + regression metrics (no sklearn). - `comparison.py`: `build_comparison()` runs both pipelines on the seed=42 test split, returns a `Comparison` dataclass. - `report.py`: Jinja2 -> HTML always; WeasyPrint -> PDF when installed. - `reports/templates/report.html.j2`: A4-formatted report with headline table, per-variant detail, tier/latency, top failures, disclosures. ccdp/api…323fee6