Merge pull request #1908 from roboflow/develop

`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 = "0.26.1rc0"
+version = "0.26.1"
 readme = "README.md"
 requires-python = ">=3.9"
 authors = [

supervision/annotators/core.py

@@ -2239,8 +2239,11 @@ class PercentageBarAnnotator(BaseAnnotator):
         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/metrics/mean_average_precision.py

@@ -1261,7 +1261,7 @@ class MeanAveragePrecision(Metric):
         for image_id, image_targets in enumerate(targets):
             if self._image_indices is not None:
                 image_id = self._image_indices[image_id]
-            for target in image_targets:
+            for target_idx, target in enumerate(image_targets):
                 xyxy = target[0]  # or xyxy = prediction[0]; xyxy[2:4] -= xyxy[0:2]
                 xywh = [xyxy[0], xyxy[1], xyxy[2] - xyxy[0], xyxy[3] - xyxy[1]]
                 # Get "area" and "iscrowd" (default 0) from data
@@ -1271,8 +1271,15 @@ class MeanAveragePrecision(Metric):
                     category_id = self._class_mapping[target[3].item()]
                 else:
                     category_id = target[3].item()
+
+                # Use area from data if available (e.g., COCO datasets)
+                # Otherwise use Detections.area property
+                area = data.get("area") if data else None
+                if area is None:
+                    area = image_targets.area[target_idx]
+
                 dict_annotation = {
-                    "area": data.get("area", 0),
+                    "area": area,
                     "iscrowd": data.get("iscrowd", 0),
                     "image_id": image_id,
                     "bbox": xywh,

test/metrics/test_mean_average_precision_area.py

@@ -0,0 +1,131 @@
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from supervision.detection.core import Detections
+from supervision.metrics.mean_average_precision import MeanAveragePrecision
+
+
+class TestMeanAveragePrecisionArea:
+    """Test area calculation in MeanAveragePrecision."""
+
+    @pytest.mark.parametrize(
+        "xyxy, expected_areas, expected_size_maps",
+        [
+            (
+                np.array(
+                    [
+                        [10, 10, 40, 40],  # Small: 900
+                        [100, 100, 200, 150],  # Medium: 5000
+                        [300, 300, 500, 400],  # Large: 20000
+                    ],
+                    dtype=np.float32,
+                ),
+                [900.0, 5000.0, 20000.0],
+                {"small": True, "medium": True, "large": True},
+            ),
+            (
+                np.array([[0, 0, 10, 10]], dtype=np.float32),  # Small: 100
+                [100.0],
+                {"small": True, "medium": False, "large": False},
+            ),
+            (
+                np.array([[0, 0, 50, 50]], dtype=np.float32),  # Medium: 2500
+                [2500.0],
+                {"small": False, "medium": True, "large": False},
+            ),
+            (
+                np.array([[0, 0, 100, 100]], dtype=np.float32),  # Large: 10000
+                [10000.0],
+                {"small": False, "medium": False, "large": True},
+            ),
+        ],
+    )
+    def test_area_calculation_and_size_specific_map(
+        self, xyxy, expected_areas, expected_size_maps
+    ):
+        """Test area calculation and size-specific mAP functionality."""
+        gt = Detections(
+            xyxy=xyxy,
+            class_id=np.arange(len(xyxy)),
+        )
+        pred = Detections(
+            xyxy=gt.xyxy.copy(),
+            class_id=gt.class_id.copy(),
+            confidence=np.full(len(xyxy), 0.9),
+        )
+
+        map_metric = MeanAveragePrecision()
+        map_metric.update([pred], [gt])
+
+        # Test area calculation
+        prepared_targets = map_metric._prepare_targets(map_metric._targets_list)
+        areas = [ann["area"] for ann in prepared_targets["annotations"]]
+        assert np.allclose(areas, expected_areas), (
+            f"Expected {expected_areas}, got {areas}"
+        )
+
+        # Test size-specific mAP
+        result = map_metric.compute()
+
+        if expected_size_maps["small"]:
+            assert result.small_objects.map50 > 0.9, (
+                "Small objects should have high mAP"
+            )
+        else:
+            assert result.small_objects.map50 == -1.0, (
+                "Small objects should have no data"
+            )
+
+        if expected_size_maps["medium"]:
+            assert result.medium_objects.map50 > 0.9, (
+                "Medium objects should have high mAP"
+            )
+        else:
+            assert result.medium_objects.map50 == -1.0, (
+                "Medium objects should have no data"
+            )
+
+        if expected_size_maps["large"]:
+            assert result.large_objects.map50 > 0.9, (
+                "Large objects should have high mAP"
+            )
+        else:
+            assert result.large_objects.map50 == -1.0, (
+                "Large objects should have no data"
+            )
+
+    def test_area_preserved_from_data(self):
+        """Test that area from data field is preserved (COCO case)."""
+        gt = Detections(
+            xyxy=np.array(
+                [[100, 100, 200, 150]], dtype=np.float32
+            ),  # Would calculate to 5000
+            class_id=np.array([0]),
+        )
+        # Override with custom area
+        gt.data = {"area": np.array([3000.0])}
+
+        pred = Detections(
+            xyxy=gt.xyxy.copy(),
+            class_id=gt.class_id.copy(),
+            confidence=np.array([0.9]),
+        )
+        pred.data = {"area": np.array([3000.0])}
+
+        map_metric = MeanAveragePrecision()
+        map_metric.update([pred], [gt])
+
+        prepared_targets = map_metric._prepare_targets(map_metric._targets_list)
+        used_area = prepared_targets["annotations"][0]["area"]
+
+        assert np.allclose(used_area, 3000.0), (
+            f"Should use provided area 3000.0, got {used_area}"
+        )
+
+        # Verify it's different from what would be calculated
+        calculated_area = (200 - 100) * (150 - 100)  # 100 * 50 = 5000
+        assert not np.allclose(used_area, calculated_area), (
+            "Should use provided area, not calculated"
+        )