squash history to reclaim storage from re-pushed image blobs

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - object-detection
+language: [en]
+tags:
+  - document-ai
+  - form-field-detection
+  - forms
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: data/test-*
+---
+
+# form-field-v1-benchmark
+
+The evaluation set behind the **`form-field-v1`** form-field detectors and the
+[leaderboard](https://huggingface.co/spaces/nutrientdocs/form-field-v1-leaderboard) — **5,914 form pages** with
+**126,938 annotated fields** (`Text`, `Choice` = checkbox/radio, `Signature`), across three variants:
+
+| variant | images | fields | notes |
+|---|---|---|---|
+| `empty` | 2,334 | 49,171 | blank forms |
+| `filled` | 1,826 | 39,797 | digitally-typed values |
+| `handwritten` | 1,754 | 37,970 | handwritten (ink) values |
+
+A Nutrient dataset, released under **CC-BY-4.0**.
+
+- 🎯 [Demo](https://huggingface.co/spaces/nutrientdocs/form-field-v1-demo) ·
+  🏆 [Leaderboard](https://huggingface.co/spaces/nutrientdocs/form-field-v1-leaderboard)
+
+## Columns
+- `image` — the rendered page (RGB).
+- `page_id` — stable id; ends in `_empty` / `_filled` / `_handwritten`.
+- `variant` — `empty` | `filled` | `handwritten`.
+- `width`, `height` — page pixel size.
+- `fields` — list of `{ "box": [x, y, w, h], "category": "Text"|"ChoiceButton"|"Signature" }` (page pixels).
+- `source`, `license_class` — provenance tags.
+
+## Scoring
+
+Detectors are scored with **COCO mAP50-95** (pycocotools, `maxDets [1,100,1000]`), reported overall and per
+variant.
+
+```python
+# pip install datasets pycocotools
+python score_detector.py your_predictions.json
+# predictions: [{"image_id": page_id, "category_id": 1|2|3, "score": s, "bbox": [x,y,w,h]}]
+```
+
+Reference numbers (mAP50-95) on this set — the `form-field-v1` family: medium **0.529 / 0.783 / 0.764**,
+small **0.456 / 0.716 / 0.680**, nano **0.353 / 0.560 / 0.526** (empty / filled / handwritten).
+
+## License
+
+**CC-BY-4.0.** Attribute to Nutrient. Empty pages are real blank forms; filled and handwritten values are
+synthetic. No PII.
+
+## About the author
+
+<a href="https://nutrient.io/">
+  <img src="https://avatars2.githubusercontent.com/u/1527679?v=3&s=200" height="80" />
+</a>
+
+This project is maintained and funded by [Nutrient](https://nutrient.io/) - The deterministic document infrastructure enterprises run their highest-stakes workflows on: replayable output, clear exceptions, and full audit trails on the messy, regulated documents where AI alone breaks.

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score_detector.py

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+"""Reference scorer for form-field-v1-benchmark — COCO mAP50-95 (pycocotools), per-variant + per-class.
+
+Predictions: a JSON list of {"image_id": <page_id>, "category_id": 1|2|3, "score": float, "bbox": [x,y,w,h]}
+             (category 1=Text, 2=ChoiceButton, 3=Signature; bbox in page pixels).
+
+    pip install datasets pycocotools
+    python score_detector.py preds.json                 # overall + per variant + per class
+"""
+import sys, json, contextlib, io
+from datasets import load_dataset
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+CATS = [(1, "Text"), (2, "ChoiceButton"), (3, "Signature")]
+CAT_ID = {"Text": 1, "ChoiceButton": 2, "Signature": 3}
+
+
+def build_gt(split_rows):
+    images, anns, aid = [], [], 1
+    idmap = {}
+    for i, r in enumerate(split_rows):
+        idmap[r["page_id"]] = i
+        images.append({"id": i, "file_name": r["page_id"], "width": r["width"], "height": r["height"],
+                       "variant": r["variant"]})
+        for f in r["fields"]:
+            x, y, w, h = f["box"]
+            anns.append({"id": aid, "image_id": i, "category_id": CAT_ID[f["category"]],
+                         "bbox": [x, y, w, h], "area": w * h, "iscrowd": 0}); aid += 1
+    coco = COCO(); coco.dataset = {"images": images,
+                                   "categories": [{"id": c, "name": n} for c, n in CATS], "annotations": anns}
+    with contextlib.redirect_stdout(io.StringIO()): coco.createIndex()
+    return coco, idmap
+
+
+def score(coco, dets, tag):
+    with contextlib.redirect_stdout(io.StringIO()):
+        dt = coco.loadRes(dets)
+        ev = COCOeval(coco, dt, "bbox"); ev.params.maxDets = [1, 100, 1000]
+        ev.evaluate(); ev.accumulate(); ev.summarize()
+    per = []
+    for cid, nm in CATS:
+        with contextlib.redirect_stdout(io.StringIO()):
+            e = COCOeval(coco, dt, "bbox"); e.params.maxDets = [1, 100, 1000]; e.params.catIds = [cid]
+            e.evaluate(); e.accumulate(); e.summarize()
+        per.append(f"{nm} {e.stats[0]:.3f}")
+    print(f"{tag:14s} mAP50-95={ev.stats[0]:.4f} AP50={ev.stats[1]:.4f}  ({' / '.join(per)})")
+
+
+def main():
+    preds = json.load(open(sys.argv[1]))
+    rows = list(load_dataset("nutrientdocs/form-field-v1-benchmark", split="test"))
+    # map page_id (string image_id in preds) -> integer id used by the GT
+    coco_all, idmap = build_gt(rows)
+    dets = [{**d, "image_id": idmap[d["image_id"]]} for d in preds if d["image_id"] in idmap]
+    score(coco_all, dets, "OVERALL")
+    for variant in ("empty", "filled", "handwritten"):
+        sub = [r for r in rows if r["variant"] == variant]
+        coco_v, idm_v = build_gt(sub)
+        dv = [{**d, "image_id": idm_v[d["image_id"]]} for d in preds if d["image_id"] in idm_v]
+        score(coco_v, dv, variant)
+
+
+if __name__ == "__main__":
+    main()