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+  {
+    "source": "D:\\work\\yolo\\data\\Smoke-Fire-Detection-YOLO\\test\\images\\WEB10086.jpg",
+    "label": "D:\\work\\yolo\\data\\Smoke-Fire-Detection-YOLO\\test\\labels\\WEB10086.txt",
+    "review_image": "D:\\work\\yolo\\runs\\audit\\missing_label_review_ge_0.50\\038_WEB10086.jpg",
+    "predictions": []
+  },
+  {
+    "source": "D:\\work\\yolo\\data\\Smoke-Fire-Detection-YOLO\\train\\images\\WEB02742.jpg",
+    "label": "D:\\work\\yolo\\data\\Smoke-Fire-Detection-YOLO\\train\\labels\\WEB02742.txt",
+    "review_image": "D:\\work\\yolo\\runs\\audit\\missing_label_review_ge_0.50\\039_WEB02742.jpg",
+    "predictions": []
+  }
+]

runs/audit/render_missing_label_candidates.py

@@ -0,0 +1,69 @@
+import csv
+from pathlib import Path
+
+import cv2
+from ultralytics import YOLO
+
+weights = Path(r"D:\work\yolo\runs\detect\smoke_fire_yolo11s_v1-4\weights\best.pt")
+candidates_path = Path(r"D:\work\yolo\runs\audit\suspected_missing_labels.csv")
+output_dir = Path(r"D:\work\yolo\runs\audit\missing_label_review_ge_0.50")
+threshold = 0.50
+class_names = ["smoke", "fire"]
+
+with candidates_path.open(encoding="utf-8", newline="") as csv_file:
+    candidates = list(csv.DictReader(csv_file))
+image_paths = []
+for row in candidates:
+    if float(row["confidence"]) < threshold:
+        continue
+    image_path = Path(row["image"])
+    if image_path not in image_paths:
+        image_paths.append(image_path)
+
+model = YOLO(str(weights))
+output_dir.mkdir(parents=True, exist_ok=True)
+manifest = []
+for index, image_path in enumerate(image_paths, start=1):
+    image = cv2.imread(str(image_path))
+    if image is None:
+        continue
+    height, width = image.shape[:2]
+    label_path = image_path.parents[1] / "labels" / f"{image_path.stem}.txt"
+    labeled = image.copy()
+    label_lines = [line for line in label_path.read_text(encoding="utf-8").splitlines() if line.strip()] if label_path.is_file() else []
+    for line in label_lines:
+        values = line.split()
+        class_id = int(float(values[0]))
+        x_center, y_center, box_width, box_height = map(float, values[1:])
+        x1 = int((x_center - box_width / 2) * width)
+        y1 = int((y_center - box_height / 2) * height)
+        x2 = int((x_center + box_width / 2) * width)
+        y2 = int((y_center + box_height / 2) * height)
+        cv2.rectangle(labeled, (x1, y1), (x2, y2), (0, 220, 0), 3)
+        cv2.putText(labeled, class_names[class_id], (x1, max(28, y1 - 7)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 220, 0), 2)
+
+    predicted = image.copy()
+    result = model.predict(str(image_path), imgsz=640, conf=threshold, device=0, verbose=False)[0]
+    predictions = []
+    for box in result.boxes:
+        class_id = int(box.cls.item())
+        confidence = float(box.conf.item())
+        x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
+        predictions.append({"class": class_names[class_id], "confidence": round(confidence, 6), "xyxy": [x1, y1, x2, y2]})
+        cv2.rectangle(predicted, (x1, y1), (x2, y2), (0, 0, 255), 3)
+        cv2.putText(predicted, f"{class_names[class_id]} {confidence:.3f}", (x1, max(28, y1 - 7)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
+
+    canvas = cv2.hconcat([labeled, predicted])
+    cv2.rectangle(canvas, (0, 0), (canvas.shape[1], 58), (255, 255, 255), -1)
+    cv2.putText(canvas, f"Original label: {image_path.name}", (20, 38), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 120, 0), 2)
+    cv2.putText(canvas, "YOLO11s prediction", (width + 20, 38), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 180), 2)
+    output_path = output_dir / f"{index:03d}_{image_path.stem}.jpg"
+    if not cv2.imwrite(str(output_path), canvas):
+        raise OSError(output_path)
+    manifest.append({"source": str(image_path), "label": str(label_path), "review_image": str(output_path), "predictions": predictions})
+    print(f"rendered {index}/{len(image_paths)} {image_path.name}", flush=True)
+
+manifest_path = output_dir / "manifest.json"
+import json
+manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8")
+print(f"completed={len(manifest)} manifest={manifest_path}")

runs/audit/scan_missing_labels.py

@@ -0,0 +1,80 @@
+import csv
+import json
+from collections import Counter
+from pathlib import Path
+
+from ultralytics import YOLO
+
+root = Path(r"D:\work\yolo\data\Smoke-Fire-Detection-YOLO")
+weights = Path(r"D:\work\yolo\runs\detect\smoke_fire_yolo11s_v1-4\weights\best.pt")
+audit_dir = Path(r"D:\work\yolo\runs\audit")
+csv_output = audit_dir / "suspected_missing_labels.csv"
+json_output = audit_dir / "suspected_missing_labels.json"
+image_extensions = (".jpg", ".jpeg", ".png", ".bmp", ".webp")
+
+images: list[str] = []
+image_splits: dict[str, str] = {}
+for split in ("train", "val", "test"):
+    for label_path in sorted((root / split / "labels").glob("*.txt")):
+        if label_path.read_text(encoding="utf-8", errors="replace").strip():
+            continue
+        for extension in image_extensions:
+            image_path = root / split / "images" / f"{label_path.stem}{extension}"
+            if image_path.is_file():
+                normalized = str(image_path.resolve())
+                images.append(normalized)
+                image_splits[normalized.lower()] = split
+                break
+
+model = YOLO(str(weights))
+rows: list[dict[str, object]] = []
+chunk_size = 32
+for offset in range(0, len(images), chunk_size):
+    chunk = images[offset : offset + chunk_size]
+    results = model.predict(source=chunk, imgsz=640, conf=0.25, device=0, batch=16, verbose=False)
+    for source_path, result in zip(chunk, results, strict=True):
+        boxes = result.boxes
+        if boxes is None or len(boxes) == 0:
+            continue
+        image_path = source_path
+        for class_id, confidence, xyxy in zip(boxes.cls.tolist(), boxes.conf.tolist(), boxes.xyxy.tolist()):
+            class_index = int(class_id)
+            rows.append({
+                "split": image_splits.get(image_path.lower(), "unknown"),
+                "image": image_path,
+                "class_id": class_index,
+                "class_name": model.names[class_index],
+                "confidence": round(float(confidence), 6),
+                "x1": round(float(xyxy[0]), 2),
+                "y1": round(float(xyxy[1]), 2),
+                "x2": round(float(xyxy[2]), 2),
+                "y2": round(float(xyxy[3]), 2),
+            })
+    if offset and offset % 1024 == 0:
+        print(f"scanned {offset}/{len(images)}", flush=True)
+
+rows.sort(key=lambda row: float(row["confidence"]), reverse=True)
+fieldnames = ["split", "image", "class_id", "class_name", "confidence", "x1", "y1", "x2", "y2"]
+with csv_output.open("w", encoding="utf-8", newline="") as csv_file:
+    writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
+    writer.writeheader()
+    writer.writerows(rows)
+
+thresholds = {}
+for threshold in (0.25, 0.5, 0.7, 0.85):
+    selected = [row for row in rows if float(row["confidence"]) >= threshold]
+    thresholds[str(threshold)] = {
+        "detections": len(selected),
+        "images": len({row["image"] for row in selected}),
+        "smoke_images": len({row["image"] for row in selected if row["class_name"] == "smoke"}),
+        "fire_images": len({row["image"] for row in selected if row["class_name"] == "fire"}),
+        "by_split": dict(Counter(str(row["split"]) for row in selected)),
+    }
+report = {
+    "empty_label_images": len(images),
+    "thresholds": thresholds,
+    "top_candidates": rows[:50],
+    "csv": str(csv_output),
+}
+json_output.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+print(json.dumps(report, ensure_ascii=False, indent=2))

runs/audit/smoke_fire_labels.json

@@ -0,0 +1,324 @@
+{
+  "dataset_root": "D:\\work\\yolo\\data\\Smoke-Fire-Detection-YOLO",
+  "classes": {
+    "0": "smoke",
+    "1": "fire"
+  },
+  "splits": {
+    "train": {
+      "split": "train",
+      "images": 14122,
+      "labels": 14122,
+      "matched_pairs": 14122,
+      "missing_images": [],
+      "missing_labels": [],
+      "unreadable_images": [],
+      "empty_labels": 6458,
+      "total_instances": 17432,
+      "classes": {
+        "smoke": {
+          "class_id": 0,
+          "instances": 7794,
+          "instance_fraction": 0.44710876548875633,
+          "width": {
+            "p01": 0.0203125,
+            "p05": 0.03125,
+            "p50": 0.3566431033430942,
+            "p95": 0.978125,
+            "p99": 0.9908704856787047
+          },
+          "height": {
+            "p01": 0.030555555555555558,
+            "p05": 0.04861111111111111,
+            "p50": 0.3763762626262626,
+            "p95": 0.8416666666666668,
+            "p99": 0.9744318181818182
+          },
+          "area": {
+            "p01": 0.0006684027777777779,
+            "p05": 0.0017649739583333339,
+            "p50": 0.14010091145833334,
+            "p95": 0.7182144953051643,
+            "p99": 0.9138081597222223
+          },
+          "aspect_ratio": {
+            "p01": 0.2708129699248121,
+            "p05": 0.38062669606512894,
+            "p50": 0.9457014574507893,
+            "p95": 2.1509072952779897,
+            "p99": 3.376300016137921
+          },
+          "small_area_lt_1_percent": 1576
+        },
+        "fire": {
+          "class_id": 1,
+          "instances": 9638,
+          "instance_fraction": 0.5528912345112437,
+          "width": {
+            "p01": 0.01015625,
+            "p05": 0.01640625,
+            "p50": 0.0671875,
+            "p95": 0.36875,
+            "p99": 0.7077182387312184
+          },
+          "height": {
+            "p01": 0.012610500610500612,
+            "p05": 0.0205791788856305,
+            "p50": 0.07777777777777778,
+            "p95": 0.3648212129544395,
+            "p99": 0.6592166036546933
+          },
+          "area": {
+            "p01": 0.00016203703703703703,
+            "p05": 0.00038837566264618293,
+            "p50": 0.005419988037771002,
+            "p95": 0.10975683593749996,
+            "p99": 0.3154305121527763
+          },
+          "aspect_ratio": {
+            "p01": 0.25,
+            "p05": 0.36411975650916106,
+            "p50": 0.8266485507246377,
+            "p95": 2.5934229192938156,
+            "p99": 4.644447004608296
+          },
+          "small_area_lt_1_percent": 6209
+        }
+      },
+      "image_dimensions": {
+        "width": {
+          "p01": 202.0,
+          "p05": 480.0,
+          "p50": 1080.0,
+          "p95": 1280.0,
+          "p99": 1920.0
+        },
+        "height": {
+          "p01": 288.0,
+          "p05": 352.0,
+          "p50": 705.0,
+          "p95": 749.0,
+          "p99": 1080.0
+        }
+      },
+      "issues": {
+        "x_bounds": 130,
+        "y_bounds": 103,
+        "non_positive_size": 13,
+        "x_bounds,y_bounds": 22
+      },
+      "near_duplicate_files": []
+    },
+    "val": {
+      "split": "val",
+      "images": 3099,
+      "labels": 3099,
+      "matched_pairs": 3099,
+      "missing_images": [],
+      "missing_labels": [],
+      "unreadable_images": [],
+      "empty_labels": 1375,
+      "total_instances": 3932,
+      "classes": {
+        "smoke": {
+          "class_id": 0,
+          "instances": 1756,
+          "instance_fraction": 0.44659206510681587,
+          "width": {
+            "p01": 0.01953125,
+            "p05": 0.033398437499999996,
+            "p50": 0.367489837398374,
+            "p95": 0.978125,
+            "p99": 0.9905925707547171
+          },
+          "height": {
+            "p01": 0.029166666666666667,
+            "p05": 0.051388888888888894,
+            "p50": 0.3868817204301075,
+            "p95": 0.8509030032467533,
+            "p99": 0.9737224264705882
+          },
+          "area": {
+            "p01": 0.0006148003472222223,
+            "p05": 0.0019029405381944446,
+            "p50": 0.15472994353702563,
+            "p95": 0.7186140680638037,
+            "p99": 0.8928481691919194
+          },
+          "aspect_ratio": {
+            "p01": 0.26327884615384617,
+            "p05": 0.39345044378698224,
+            "p50": 0.9305993690851734,
+            "p95": 2.197773279352226,
+            "p99": 3.3725553551117837
+          },
+          "small_area_lt_1_percent": 320
+        },
+        "fire": {
+          "class_id": 1,
+          "instances": 2176,
+          "instance_fraction": 0.5534079348931842,
+          "width": {
+            "p01": 0.0109375,
+            "p05": 0.017457755632582325,
+            "p50": 0.066328125,
+            "p95": 0.375390625,
+            "p99": 0.700234375
+          },
+          "height": {
+            "p01": 0.01388888888888889,
+            "p05": 0.022222222222222223,
+            "p50": 0.0795957361222848,
+            "p95": 0.3423611111111111,
+            "p99": 0.583418501048218
+          },
+          "area": {
+            "p01": 0.00018176379504504503,
+            "p05": 0.000428799715909091,
+            "p50": 0.005261230468750001,
+            "p95": 0.11621071829405165,
+            "p99": 0.29433426057449497
+          },
+          "aspect_ratio": {
+            "p01": 0.24368708053691274,
+            "p05": 0.3605205829326923,
+            "p50": 0.8172757475083057,
+            "p95": 2.7240403846153844,
+            "p99": 4.374748607673267
+          },
+          "small_area_lt_1_percent": 1392
+        }
+      },
+      "image_dimensions": {
+        "width": {
+          "p01": 204.0,
+          "p05": 480.0,
+          "p50": 1024.0,
+          "p95": 1280.0,
+          "p99": 1920.0
+        },
+        "height": {
+          "p01": 291.98,
+          "p05": 352.0,
+          "p50": 698.0,
+          "p95": 763.2999999999997,
+          "p99": 1080.0
+        }
+      },
+      "issues": {
+        "x_bounds": 35,
+        "y_bounds": 25,
+        "x_bounds,y_bounds": 2,
+        "non_positive_size": 1
+      },
+      "near_duplicate_files": []
+    },
+    "test": {
+      "split": "test",
+      "images": 4306,
+      "labels": 4306,
+      "matched_pairs": 4306,
+      "missing_images": [],
+      "missing_labels": [],
+      "unreadable_images": [],
+      "empty_labels": 2005,
+      "total_instances": 5193,
+      "classes": {
+        "smoke": {
+          "class_id": 0,
+          "instances": 2315,
+          "instance_fraction": 0.44579241286347004,
+          "width": {
+            "p01": 0.0171875,
+            "p05": 0.0296875,
+            "p50": 0.35520833333333335,
+            "p95": 0.978125,
+            "p99": 0.9921145833333334
+          },
+          "height": {
+            "p01": 0.02638888888888889,
+            "p05": 0.044444444444444446,
+            "p50": 0.375,
+            "p95": 0.8405555555555541,
+            "p99": 0.9658431085043989
+          },
+          "area": {
+            "p01": 0.0005388020833333333,
+            "p05": 0.0014906486742424243,
+            "p50": 0.14241458333333334,
+            "p95": 0.7164741103575497,
+            "p99": 0.9133488775143681
+          },
+          "aspect_ratio": {
+            "p01": 0.2813657407407407,
+            "p05": 0.37762266059994576,
+            "p50": 0.9680792682926829,
+            "p95": 2.2115971444568867,
+            "p99": 3.2713018096423445
+          },
+          "small_area_lt_1_percent": 499
+        },
+        "fire": {
+          "class_id": 1,
+          "instances": 2878,
+          "instance_fraction": 0.55420758713653,
+          "width": {
+            "p01": 0.009375000000000001,
+            "p05": 0.016303784547738693,
+            "p50": 0.066328125,
+            "p95": 0.3617916666666668,
+            "p99": 0.661491185897436
+          },
+          "height": {
+            "p01": 0.012962962962962964,
+            "p05": 0.019518229166666665,
+            "p50": 0.07862862169801331,
+            "p95": 0.36975207684319855,
+            "p99": 0.6305445219966161
+          },
+          "area": {
+            "p01": 0.00014637632593273098,
+            "p05": 0.00036123770254629635,
+            "p50": 0.005331114969135802,
+            "p95": 0.11221848513321647,
+            "p99": 0.3127092000753551
+          },
+          "aspect_ratio": {
+            "p01": 0.24188545296167246,
+            "p05": 0.36467155612244895,
+            "p50": 0.8289473684210528,
+            "p95": 2.5461363636363643,
+            "p99": 4.116392276422765
+          },
+          "small_area_lt_1_percent": 1860
+        }
+      },
+      "image_dimensions": {
+        "width": {
+          "p01": 204.0,
+          "p05": 480.0,
+          "p50": 1047.5,
+          "p95": 1280.0,
+          "p99": 1920.0
+        },
+        "height": {
+          "p01": 240.0,
+          "p05": 352.0,
+          "p50": 683.0,
+          "p95": 768.0,
+          "p99": 1080.0
+        }
+      },
+      "issues": {
+        "non_positive_size": 4,
+        "x_bounds": 35,
+        "y_bounds": 18,
+        "coordinate_range,x_bounds": 4,
+        "coordinate_range,x_bounds,y_bounds": 2,
+        "coordinate_range,y_bounds": 2,
+        "x_bounds,y_bounds": 1
+      },
+      "near_duplicate_files": []
+    }
+  }
+}

runs/audit/suspected_missing_labels.csv

@@ -0,0 +1,176 @@
+split,image,class_id,class_name,confidence,x1,y1,x2,y2
+val,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\val\images\WEB02503.jpg,0,smoke,0.831814,8.45,1.61,487.99,355.66
+val,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\val\images\PublicDataset00895.jpg,0,smoke,0.825629,402.33,2.94,800.0,181.93
+test,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\test\images\WEB10145.jpg,1,fire,0.776786,0.0,17.78,471.98,366.27
+test,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\test\images\WEB10378.jpg,0,smoke,0.771172,1.0,11.53,796.85,399.8
+val,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\val\images\WEB02317.jpg,0,smoke,0.734445,15.89,0.0,533.85,211.81
+val,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\val\images\WEB02476.jpg,0,smoke,0.711665,0.32,6.54,378.97,229.19
+train,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\train\images\WEB02822.jpg,0,smoke,0.704389,752.98,11.22,1115.73,760.9
+train,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\train\images\WEB02465.jpg,0,smoke,0.688081,467.39,1024.26,1867.1,1746.11
+train,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\train\images\AoF06233.jpg,0,smoke,0.645559,1091.73,97.33,1212.16,169.22
+val,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\val\images\AoF00115.jpg,0,smoke,0.641924,212.21,17.07,405.64,69.88
+test,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\test\images\PublicDataset01303.jpg,1,fire,0.624514,128.64,97.37,233.21,286.97
+test,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\test\images\WEB10106.jpg,0,smoke,0.621397,13.56,51.3,757.03,284.54
+train,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\train\images\AoF00117.jpg,0,smoke,0.61882,198.27,16.01,400.17,69.17
+test,D:\work\yolo\data\Smoke-Fire-Detection-YOLO\test\images\WEB10369.jpg,0,smoke,0.618061,471.54,183.12,488.83,210.97
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