fix(pre_commit): 🎨 auto format pre-commit hooks

Author: pre-commit-ci[bot]

examples/compact_mask/README.md

@@ -198,7 +198,7 @@ reported as theoretical `NxHxW` bytes.
 - **Compact actual** — `tracemalloc` peak during `CompactMask.from_dense()`, including Python object overhead (~2x theoretical for small object counts)
 - **Mem x** — dense / compact theoretical ratio
 - **Area x** — `.area` speedup; RLE sums True-pixel counts with no materialisation
-- **Annot ×** — `MaskAnnotator` speedup; crop-paint avoids full-frame allocation
+- **Annot x** — `MaskAnnotator` speedup; crop-paint avoids full-frame allocation
 - **N/A** — dense timing skipped (array > 12 GB)
 
 All non-skipped scenarios pass: pixel-perfect annotation, exact area,

examples/compact_mask/benchmark.py

@@ -481,19 +481,15 @@ def stage_merge(
     half = len(det_compact) // 2
     compact_a, compact_b = det_compact[:half], det_compact[half:]
 
-    compact_merge_s = time_reps(
-        lambda: sv.Detections.merge([compact_a, compact_b])
-    )
+    compact_merge_s = time_reps(lambda: sv.Detections.merge([compact_a, compact_b]))
     if dense_skipped or det_dense is None:
         return math.nan, compact_merge_s, None
 
     dense_a, dense_b = det_dense[:half], det_dense[half:]
     merged_d = sv.Detections.merge([dense_a, dense_b])
     merged_c = sv.Detections.merge([compact_a, compact_b])
     merge_ok = bool(np.allclose(merged_d.area, merged_c.area))
-    dense_merge_s = time_reps(
-        lambda: sv.Detections.merge([dense_a, dense_b])
-    )
+    dense_merge_s = time_reps(lambda: sv.Detections.merge([dense_a, dense_b]))
     return dense_merge_s, compact_merge_s, merge_ok
 
 

examples/time_in_zone/README.md

@@ -222,7 +222,7 @@ Script to run object detection on an RTSP stream using the RF-DETR model.
 - `--model_size`: RF-DETR backbone size to load — choose from 'nano', 'small', 'medium', 'base', or 'large' (default 'medium').
 - `--device`: Compute device to run the model on ('cpu', 'mps', or 'cuda'; default 'cpu').
 - `--classes`: Space-separated list of class IDs to track. Leave empty to track all classes.
-- `--confidence_threshold`: Minimum confidence score for a detection to be kept, range 0–1 (default 0.3).
+- `--confidence_threshold`: Minimum confidence score for a detection to be kept, range 0-1 (default 0.3).
 - `--iou_threshold`: IOU threshold applied during non-max suppression (default 0.7).
 - `--resolution`: Shortest-side input resolution supplied to the model. The script will round it to the nearest valid multiple (default 640).
 

src/supervision/detection/utils/masks.py

@@ -365,7 +365,7 @@ def filter_segments_by_distance(
 
         ```
 
-        The nearby 2×2 block at columns 6–7 is kept because its edge distance
+        The nearby 2x2 block at columns 6-7 is kept because its edge distance
         is within 3 pixels. The distant block at columns 9-10 is removed.
     """  # noqa E501 // docs
     if mask.dtype != bool:

tests/detection/test_compact_mask.py

@@ -430,13 +430,13 @@ def test_repack_tightens_loose_bbox(self) -> None:
         """repack() shrinks the crop to the minimal True-pixel rectangle."""
         h, w = 20, 20
         masks = np.zeros((1, h, w), dtype=bool)
-        masks[0, 5:10, 6:12] = True  # True block at (5,6)–(9,11)
+        masks[0, 5:10, 6:12] = True  # True block at (5,6)-(9,11)
 
         # Deliberately loose bbox covers full image.
         xyxy = np.array([[0, 0, w - 1, h - 1]], dtype=np.float32)
         cm = CompactMask.from_dense(masks, xyxy, image_shape=(h, w))
 
-        # Before repack: crop is the full 20×20 image.
+        # Before repack: crop is the full 20x20 image.
         assert cm._crop_shapes[0].tolist() == [20, 20]
 
         repacked = cm.repack()
@@ -448,7 +448,7 @@ def test_repack_tightens_loose_bbox(self) -> None:
         np.testing.assert_array_equal(repacked.to_dense(), masks)
 
     def test_repack_preserves_all_false_mask(self) -> None:
-        """repack() normalises an all-False mask to a 1×1 crop."""
+        """repack() normalises an all-False mask to a 1x1 crop."""
         h, w = 10, 10
         masks = np.zeros((2, h, w), dtype=bool)
         masks[1, 3:6, 3:6] = True  # only mask 1 is non-empty