diff --git a/README.md b/README.md
index 4b1ff9d..bea1fcc 100644
--- a/README.md
+++ b/README.md
@@ -76,6 +76,17 @@ context leaks onto the wrong card:
   AGPL-3.0 too. Fine for personal/research dataset building; if this
   ever ships in a product, revisit.
 
+## QC: auditing an existing collection
+
+`audit_stills.py` runs the same detector over a directory of stills and
+reports how many contain no face at all — e.g. measuring the damage the
+old person-proxy pipeline did:
+
+```bash
+python audit_stills.py -I ~/Documents/image-training-datasets/gabriel-dropout \
+    --gpu 1 --write-report old_run_report.csv
+```
+
 ## Verification
 
 Run `--preview-dir` and eyeball the `_ann.png` files: every saved frame
diff --git a/audit_stills.py b/audit_stills.py
new file mode 100644
index 0000000..7908cd0
--- /dev/null
+++ b/audit_stills.py
@@ -0,0 +1,107 @@
+#!/usr/bin/env python3
+"""Audit an existing still collection with the anime-face detector:
+how many saved images actually contain a qualifying face?
+
+Useful for measuring how much junk the old person-proxy pipeline left
+behind, and as a QC pass over any facesaver output.
+
+    python audit_stills.py -I <stills_dir> --gpu 1 [--write-report report.csv]
+"""
+
+import argparse
+import csv
+import os
+import sys
+
+
+def parse_args():
+    p = argparse.ArgumentParser(
+        description=(__doc__ or "Audit stills for anime faces.").split("\n")[0])
+    p.add_argument("-I", "--input-dir", required=True,
+                   help="Directory of still images to audit.")
+    p.add_argument("--model",
+                   default=os.path.join(
+                       os.path.dirname(os.path.abspath(__file__)),
+                       "models", "yolov8x6_animeface.pt"))
+    p.add_argument("--gpu", default="1",
+                   help="GPU index, 'cpu', or 'auto'.")
+    p.add_argument("--conf", type=float, default=0.3)
+    p.add_argument("--imgsz", type=int, default=1280)
+    p.add_argument("--batch", type=int, default=8)
+    p.add_argument("--write-report", default=None,
+                   help="Optional CSV: filename, n_faces, max_w, max_h, "
+                        "ok (1/0).")
+    return p.parse_args()
+
+
+def main():
+    args = parse_args()
+    if args.gpu.lower() == "cpu":
+        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+        device = "cpu"
+    elif args.gpu.lower() != "auto":
+        os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
+        device = "cuda"
+    else:
+        device = None
+
+    import cv2
+    import numpy as np
+    from ultralytics import YOLO
+
+    exts = (".png", ".jpg", ".jpeg", ".webp", ".bmp")
+    files = sorted(f for f in os.listdir(args.input_dir)
+                   if f.lower().endswith(exts))
+    if not files:
+        print("No images found.")
+        sys.exit(1)
+    print(f"Auditing {len(files)} images with "
+          f"{os.path.basename(args.model)} on {device or 'auto'}")
+
+    model = YOLO(args.model)
+
+    rows = []
+    no_face = 0
+    # stream in chunks so 1000s of 1080p pngs don't eat RAM
+    for i in range(0, len(files), args.batch):
+        chunk = files[i:i + args.batch]
+        imgs, kept = [], []
+        for f in chunk:
+            img = cv2.imread(os.path.join(args.input_dir, f))
+            if img is None:
+                print(f"  WARN unreadable: {f}")
+                continue
+            imgs.append(img)
+            kept.append(f)
+        results = model.predict(imgs, conf=args.conf, imgsz=args.imgsz,
+                                device=device, verbose=False)
+        for f, res in zip(kept, results):
+            n, mw, mh = 0, 0, 0
+            if res.boxes is not None and len(res.boxes):
+                xyxy = res.boxes.xyxy.cpu().numpy()
+                n = len(xyxy)
+                for (x1, y1, x2, y2) in xyxy:
+                    mw = max(mw, int(x2 - x1))
+                    mh = max(mh, int(y2 - y1))
+            ok = 1 if n > 0 else 0
+            if not ok:
+                no_face += 1
+                print(f"  NO FACE: {f}")
+            rows.append((f, n, mw, mh, ok))
+        done = min(i + args.batch, len(files))
+        print(f"  {done}/{len(files)}", flush=True)
+
+    print()
+    print(f"Total: {len(rows)}   with face: {len(rows) - no_face}   "
+          f"NO face: {no_face} ({100.0 * no_face / max(1, len(rows)):.1f}%)")
+
+    if args.write_report:
+        with open(args.write_report, "w", newline="") as fh:
+            w = csv.writer(fh)
+            w.writerow(["filename", "n_faces", "max_w", "max_h", "ok"])
+            w.writerows(rows)
+        print(f"Report: {args.write_report}")
+
+
+if __name__ == "__main__":
+    main()