supervision-0.26.1 release

.github/CODEOWNERS

@@ -1,4 +1,4 @@
 # These owners will be the default owners for everything in
 # the repo. They will be requested for review when someone
 # opens a pull request.
-*   @SkalskiP @onuralpszr
+*   @SkalskiP @soumik12345

docs/changelog.md

@@ -1,5 +1,17 @@
 # Changelog
 
+### 0.26.1 <small>Jul 22, 2025</small>
+
+- Fixed [1894](https://github.com/roboflow/supervision/pull/1894): Error in [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision) where the area used for size-specific evaluation (small / medium / large) was always zero unless explicitly provided in `sv.Detections.data`.
+
+- Fixed [1895](https://github.com/roboflow/supervision/pull/1895): `ID=0` bug in [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision) where objects were getting `0.0` mAP despite perfect IoU matches due to a bug in annotation ID assignment.
+
+- Fixed [1898](https://github.com/roboflow/supervision/pull/1898): Issue where [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision) could return negative values when certain object size categories have no data.
+
+- Fixed [1901](https://github.com/roboflow/supervision/pull/1901): `match_metric` support for [`sv.Detections.with_nms`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.detection.core.Detections.with_nms).
+
+- Fixed [1906](https://github.com/roboflow/supervision/pull/1906): `border_thickness` parameter usage for [`sv.PercentageBarAnnotator`](https://supervision.roboflow.com/0.26.1/metrics/mean_average_precision/#supervision.annotators.core.PercentageBarAnnotator).
+
 ### 0.26.0 <small>Jul 16, 2025</small>
 
 !!! failure "Removed"
@@ -153,7 +165,7 @@
 
 - Changed [#1786](https://github.com/roboflow/supervision/pull/1786): Significantly improved the speed of HSV color mapping in [`sv.HeatMapAnnotator`](https://supervision.roboflow.com/0.26.0/detection/annotators/#supervision.annotators.core.HeatMapAnnotator), achieving approximately 28x faster performance on 1920x1080 frames.
 
-- Fix [#1834](https://github.com/roboflow/supervision/pull/1834): Supervision’s [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.26.0/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision) is now fully aligned with [pycocotools](https://github.com/ppwwyyxx/cocoapi), the official COCO evaluation tool, ensuring accurate and standardized metrics. This update enabled us to launch a new version of the [Computer Vision Model Leaderboard](https://leaderboard.roboflow.com/).
+- Fixed [#1834](https://github.com/roboflow/supervision/pull/1834): Supervision’s [`sv.MeanAveragePrecision`](https://supervision.roboflow.com/0.26.0/metrics/mean_average_precision/#supervision.metrics.mean_average_precision.MeanAveragePrecision) is now fully aligned with [pycocotools](https://github.com/ppwwyyxx/cocoapi), the official COCO evaluation tool, ensuring accurate and standardized metrics. This update enabled us to launch a new version of the [Computer Vision Model Leaderboard](https://leaderboard.roboflow.com/).
 
     ```python
     import supervision as sv
@@ -173,7 +185,7 @@
     # Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.629
     ```
 
-- Fix [#1767](https://github.com/roboflow/supervision/pull/1767): Fixed losing `sv.Detections.data` when detections filtering.
+- Fixed [#1767](https://github.com/roboflow/supervision/pull/1767): Fixed losing `sv.Detections.data` when detections filtering.
 
 ### 0.25.0 <small>Nov 12, 2024</small>
 
@@ -557,9 +569,9 @@ detections = sv.Detections.from_sam(sam_result=sam_result)
 
 - Changed [#1434](https://github.com/roboflow/supervision/pull/1434): [`InferenceSlicer`](https://supervision.roboflow.com/0.23.0/detection/tools/inference_slicer/) now features an `overlap_wh` parameter, making it easier to compute slice sizes when handling overlapping slices.
 
-- Fix [#1448](https://github.com/roboflow/supervision/pull/1448): Various annotator type issues have been resolved, supporting expanded error handling.
+- Fixed [#1448](https://github.com/roboflow/supervision/pull/1448): Various annotator type issues have been resolved, supporting expanded error handling.
 
-- Fix [#1348](https://github.com/roboflow/supervision/pull/1348): Introduced a new method for [seeking to a specific video frame](https://supervision.roboflow.com/0.23.0/utils/video/#supervision.utils.video.get_video_frames_generator), addressing cases where traditional seek methods were failing. It can be enabled with `iterative_seek=True`.
+- Fixed [#1348](https://github.com/roboflow/supervision/pull/1348): Introduced a new method for [seeking to a specific video frame](https://supervision.roboflow.com/0.23.0/utils/video/#supervision.utils.video.get_video_frames_generator), addressing cases where traditional seek methods were failing. It can be enabled with `iterative_seek=True`.
 
 ```python
 import supervision as sv
@@ -572,7 +584,7 @@ for frame in sv.get_video_frames_generator(
     ...
 ```
 
-- Fix [#1424](https://github.com/roboflow/supervision/pull/1424): `plot_image` function now clearly indicates that the size is in inches.
+- Fixed [#1424](https://github.com/roboflow/supervision/pull/1424): `plot_image` function now clearly indicates that the size is in inches.
 
 !!! failure "Removed"
 
@@ -1285,7 +1297,7 @@ array([
 
 ### 0.11.1 <small>June 29, 2023</small>
 
-- Fix [#165](https://github.com/roboflow/supervision/pull/165): [`as_folder_structure`](/0.11.1/dataset/core/#supervision.dataset.core.ClassificationDataset.as_folder_structure) fails to save [`sv.ClassificationDataset`](/0.11.1/dataset/core/#classificationdataset) when it is result of inference.
+- Fixed [#165](https://github.com/roboflow/supervision/pull/165): [`as_folder_structure`](/0.11.1/dataset/core/#supervision.dataset.core.ClassificationDataset.as_folder_structure) fails to save [`sv.ClassificationDataset`](/0.11.1/dataset/core/#classificationdataset) when it is result of inference.
 
 ### 0.11.0 <small>June 28, 2023</small>
 
@@ -1331,7 +1343,7 @@ array([
 
 - Added [#162](https://github.com/roboflow/supervision/pull/162): additional `start` and `end` arguments to [`sv.get_video_frames_generator`](/0.11.0/utils/video/#get_video_frames_generator) allowing to generate frames only for a selected part of the video.
 
-- Fix [#157](https://github.com/roboflow/supervision/pull/157): incorrect loading of YOLO dataset class names from `data.yaml`.
+- Fixed [#157](https://github.com/roboflow/supervision/pull/157): incorrect loading of YOLO dataset class names from `data.yaml`.
 
 ### 0.10.0 <small>June 14, 2023</small>
 

pyproject.toml

@@ -2,7 +2,7 @@
 name = "supervision"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 license = { text = "MIT" }
-version = "0.26.0"
+version = "0.26.1"
 readme = "README.md"
 requires-python = ">=3.9"
 authors = [

supervision/annotators/core.py

@@ -2239,8 +2239,11 @@ def __init__(
         self.position: Position = position
         self.color_lookup: ColorLookup = color_lookup
 
-        if border_thickness is None:
-            self.border_thickness = int(0.15 * self.height)
+        self.border_thickness = (
+            border_thickness
+            if border_thickness is not None
+            else int(0.15 * self.height)
+        )
 
     @ensure_cv2_image_for_annotation
     def annotate(

supervision/detection/core.py

@@ -1939,8 +1939,8 @@ def with_nms(
             class_agnostic (bool): Whether to perform class-agnostic
                 non-maximum suppression. If True, the class_id of each detection
                 will be ignored. Defaults to False.
-            overlap_metric (OverlapMetric): Metric used for measuring overlap between
-                detections in slices.
+            overlap_metric (OverlapMetric): Metric used to compute the degree of
+                overlap between pairs of masks or boxes (e.g., IoU, IoS).
 
         Returns:
             Detections: A new Detections object containing the subset of detections
@@ -2003,8 +2003,8 @@ def with_nmm(
             class_agnostic (bool): Whether to perform class-agnostic
                 non-maximum merging. If True, the class_id of each detection
                 will be ignored. Defaults to False.
-            overlap_metric (OverlapMetric): Metric used for measuring overlap between
-                detections in slices.
+            overlap_metric (OverlapMetric): Metric used to compute the degree of
+                overlap between pairs of masks or boxes (e.g., IoU, IoS).
 
         Returns:
             Detections: A new Detections object containing the subset of detections

supervision/detection/tools/inference_slicer.py

@@ -9,7 +9,7 @@
 from supervision.config import ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
 from supervision.detection.utils.boxes import move_boxes, move_oriented_boxes
-from supervision.detection.utils.iou_and_nms import OverlapFilter
+from supervision.detection.utils.iou_and_nms import OverlapFilter, OverlapMetric
 from supervision.detection.utils.masks import move_masks
 from supervision.utils.image import crop_image
 from supervision.utils.internal import (
@@ -75,8 +75,8 @@ class InferenceSlicer:
             filtering or merging overlapping detections in slices.
         iou_threshold (float): Intersection over Union (IoU) threshold
             used when filtering by overlap.
-        match_metric (str): Metric used for matching detections in slices.
-            "IOU" or "IOS". Defaults "IOU".
+        overlap_metric (Union[OverlapMetric, str]): Metric used for matching detections
+            in slices.
         callback (Callable): A function that performs inference on a given image
             slice and returns detections.
         thread_workers (int): Number of threads for parallel execution.
@@ -96,7 +96,7 @@ def __init__(
         overlap_wh: tuple[int, int] | None = None,
         overlap_filter: OverlapFilter | str = OverlapFilter.NON_MAX_SUPPRESSION,
         iou_threshold: float = 0.5,
-        match_metric: str = "IOU",
+        overlap_metric: OverlapMetric | str = OverlapMetric.IOU,
         thread_workers: int = 1,
     ):
         if overlap_ratio_wh is not None:
@@ -112,7 +112,7 @@ def __init__(
 
         self.slice_wh = slice_wh
         self.iou_threshold = iou_threshold
-        self.match_metric = match_metric
+        self.overlap_metric = OverlapMetric.from_value(overlap_metric)
         self.overlap_filter = OverlapFilter.from_value(overlap_filter)
         self.callback = callback
         self.thread_workers = thread_workers
@@ -173,11 +173,11 @@ def callback(image_slice: np.ndarray) -> sv.Detections:
             return merged
         elif self.overlap_filter == OverlapFilter.NON_MAX_SUPPRESSION:
             return merged.with_nms(
-                threshold=self.iou_threshold, match_metric=self.match_metric
+                threshold=self.iou_threshold, overlap_metric=self.overlap_metric
             )
         elif self.overlap_filter == OverlapFilter.NON_MAX_MERGE:
             return merged.with_nmm(
-                threshold=self.iou_threshold, match_metric=self.match_metric
+                threshold=self.iou_threshold, overlap_metric=self.overlap_metric
             )
         else:
             warnings.warn(

supervision/detection/utils/iou_and_nms.py

@@ -164,7 +164,8 @@ def box_iou_batch(
             `shape = (N, 4)` where `N` is number of true objects.
         boxes_detection (np.ndarray): 2D `np.ndarray` representing detection boxes.
             `shape = (M, 4)` where `M` is number of detected objects.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of boxes (e.g., IoU, IoS).
 
     Returns:
         np.ndarray: Pairwise IoU of boxes from `boxes_true` and `boxes_detection`.
@@ -381,7 +382,8 @@ def _mask_iou_batch_split(
     Args:
         masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
         masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
 
     Returns:
         np.ndarray: Pairwise IoU of masks from `masks_true` and `masks_detection`.
@@ -433,7 +435,8 @@ def mask_iou_batch(
     Args:
         masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
         masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
         memory_limit (int): memory limit in MB, default is 1024 * 5 MB (5GB).
 
     Returns:
@@ -492,7 +495,8 @@ def mask_non_max_suppression(
             dimensions of each mask.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
         mask_dimension (int): The dimension to which the masks should be
             resized before computing IOU values. Defaults to 640.
 
@@ -543,7 +547,8 @@ def box_non_max_suppression(
             or `(x_min, y_min, x_max, y_max, score, class)`.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of boxes (e.g., IoU, IoS).
 
     Returns:
         np.ndarray: A boolean array indicating which predictions to keep after n
@@ -603,7 +608,8 @@ def _group_overlapping_masks(
             the predictions.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression. Defaults to 0.5.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
 
     Returns:
         list[list[int]]: Groups of prediction indices be merged.
@@ -664,7 +670,8 @@ def mask_non_max_merge(
             to use for non-maximum suppression.
         mask_dimension (int): The dimension to which the masks should be
             resized before computing IOU values. Defaults to 640.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of masks (e.g., IoU, IoS).
 
     Returns:
         np.ndarray: A boolean array indicating which predictions to keep after
@@ -717,7 +724,8 @@ def _group_overlapping_boxes(
             and the confidence scores.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression. Defaults to 0.5.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of boxes (e.g., IoU, IoS).
 
     Returns:
         list[list[int]]: Groups of prediction indices be merged.
@@ -765,7 +773,8 @@ def box_non_max_merge(
             detections of different classes to be merged.
         iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression. Defaults to 0.5.
-        overlap_metric (OverlapMetric): Metric used for matching detections in slices.
+        overlap_metric (OverlapMetric): Metric used to compute the degree of overlap
+            between pairs of boxes (e.g., IoU, IoS).
 
     Returns:
         list[list[int]]: Groups of prediction indices be merged.