bump version from 0.26.1 to 0.26.1rc0

fix:  🐞 update inference_slicer.py for improved detection handling

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

supervision/detection/core.py

@@ -1939,8 +1939,8 @@ class Detections:
             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 @@ class Detections:
             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 @@ import numpy as np
 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 @@ class InferenceSlicer:
         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 @@ class InferenceSlicer:
 
         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 @@ class InferenceSlicer:
             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.

supervision/metrics/mean_average_precision.py

@@ -57,7 +57,11 @@ class MeanAveragePrecisionResult:
 
     @property
     def map50_95(self) -> float:
-        return self.mAP_scores.mean()
+        valid_scores = self.mAP_scores[self.mAP_scores > -1]
+        if len(valid_scores) > 0:
+            return valid_scores.mean()
+        else:
+            return -1
 
     @property
     def map50(self) -> float:
@@ -421,6 +425,11 @@ class EvaluationDataset:
         if not isinstance(predictions, list):
             raise ValueError("results must be a list")
 
+        # Handle empty predictions
+        if len(predictions) == 0:
+            predictions_dataset.dataset["annotations"] = []
+            return predictions_dataset
+
         ids = [pred["image_id"] for pred in predictions]
 
         # Make sure the image ids from predictions exist in the current dataset
@@ -909,6 +918,35 @@ class COCOEvaluator:
                             np.array(score_at_recall)
                         )
 
+        self.results = {
+            "params": self.params,
+            "counts": [
+                num_iou_thresholds,
+                num_recall_thresholds,
+                num_categories,
+                num_area_ranges,
+                num_max_detections,
+            ],
+            "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            "precision": precision,
+            "recall": recall,
+            "scores": scores,
+        }
+
+        # Helper function to compute average precision while handling -1 sentinel values
+        def compute_average_precision(precision_slice):
+            """Compute average precision while handling -1 sentinel values."""
+            masked = np.ma.masked_equal(precision_slice, -1)
+            if masked.count() == 0:
+                # All values are -1 (no data)
+                return np.full(num_iou_thresholds, -1), np.full(
+                    (num_categories, num_iou_thresholds), -1
+                )
+            else:
+                mAP_scores = np.ma.filled(masked.mean(axis=(1, 2)), -1)
+                ap_per_class = np.ma.filled(masked.mean(axis=1), -1).transpose(1, 0)
+                return mAP_scores, ap_per_class
+
         # Average precision over all sizes, 100 max detections
         area_range_idx = list(ObjectSize).index(ObjectSize.ALL)
         max_100_dets_idx = self.params.max_dets.index(100)
@@ -917,10 +955,9 @@ class COCOEvaluator:
             :, :, :, area_range_idx, max_100_dets_idx
         ]
         # mAP over thresholds (dimension=num_thresholds)
-        mAP_scores_all_sizes = average_precision_all_sizes.mean(axis=(1, 2))
-        # AP per class
-        ap_per_class_all_sizes = average_precision_all_sizes.mean(axis=1).transpose(
-            1, 0
+        # Use masked array to exclude -1 values when computing mean
+        mAP_scores_all_sizes, ap_per_class_all_sizes = compute_average_precision(
+            average_precision_all_sizes
         )
 
         # Average precision for SMALL objects and 100 max detections
@@ -928,24 +965,27 @@ class COCOEvaluator:
         average_precision_small = precision[
             :, :, :, small_area_range_idx, max_100_dets_idx
         ]
-        mAP_scores_small = average_precision_small.mean(axis=(1, 2))
-        ap_per_class_small = average_precision_small.mean(axis=1).transpose(1, 0)
+        mAP_scores_small, ap_per_class_small = compute_average_precision(
+            average_precision_small
+        )
 
         # Average precision for MEDIUM objects and 100 max detections
         medium_area_range_idx = list(ObjectSize).index(ObjectSize.MEDIUM)
         average_precision_medium = precision[
             :, :, :, medium_area_range_idx, max_100_dets_idx
         ]
-        mAP_scores_medium = average_precision_medium.mean(axis=(1, 2))
-        ap_per_class_medium = average_precision_medium.mean(axis=1).transpose(1, 0)
+        mAP_scores_medium, ap_per_class_medium = compute_average_precision(
+            average_precision_medium
+        )
 
         # Average precision for LARGE objects and 100 max detections
         large_area_range_idx = list(ObjectSize).index(ObjectSize.LARGE)
         average_precision_large = precision[
             :, :, :, large_area_range_idx, max_100_dets_idx
         ]
-        mAP_scores_large = average_precision_large.mean(axis=(1, 2))
-        ap_per_class_large = average_precision_large.mean(axis=1).transpose(1, 0)
+        mAP_scores_large, ap_per_class_large = compute_average_precision(
+            average_precision_large
+        )
 
         self.results = {
             "params": self.params,
@@ -1237,7 +1277,7 @@ class MeanAveragePrecision(Metric):
                     "image_id": image_id,
                     "bbox": xywh,
                     "category_id": category_id,
-                    "id": len(annotations),  # incrementally increase the id
+                    "id": len(annotations) + 1,  # Start IDs from 1 (0 means no match)
                 }
                 annotations.append(dict_annotation)
         # Category list

test/metrics/test_mean_average_precision.py

@@ -0,0 +1,328 @@
+"""
+Tests for Mean Average Precision ID=0 bug fix
+"""
+
+import numpy as np
+
+from supervision.detection.core import Detections
+from supervision.metrics.mean_average_precision import MeanAveragePrecision
+
+
+def test_single_perfect_detection():
+    """Test that single perfect detection gets 1.0 mAP (not 0.0 due to ID=0 bug)"""
+    # Perfect detection (identical prediction and target)
+    detection = Detections(
+        xyxy=np.array([[10, 10, 50, 50]], dtype=np.float64),
+        class_id=np.array([0]),
+        confidence=np.array([0.9]),
+    )
+
+    metric = MeanAveragePrecision()
+    metric.update([detection], [detection])
+    result = metric.compute()
+
+    # Should be perfect 1.0 mAP, not 0.0 due to ID=0 bug
+    assert abs(result.map50_95 - 1.0) < 1e-6
+
+
+def test_multiple_perfect_detections():
+    """Test that multiple perfect detections get 1.0 mAP"""
+    # Multiple perfect detections in one image
+    detections = Detections(
+        xyxy=np.array(
+            [[10, 10, 50, 50], [100, 100, 140, 140], [200, 200, 240, 240]],
+            dtype=np.float64,
+        ),
+        class_id=np.array([0, 0, 0]),
+        confidence=np.array([0.9, 0.9, 0.9]),
+    )
+
+    metric = MeanAveragePrecision()
+    metric.update([detections], [detections])
+    result = metric.compute()
+
+    # Should be perfect 1.0 mAP
+    assert abs(result.map50_95 - 1.0) < 1e-6
+
+
+def test_batch_updates_perfect_detections():
+    """Test that batch updates with perfect detections get 1.0 mAP"""
+    # Single perfect detection for multiple batch updates
+    detection = Detections(
+        xyxy=np.array([[10, 10, 50, 50]], dtype=np.float64),
+        class_id=np.array([0]),
+        confidence=np.array([0.9]),
+    )
+
+    metric = MeanAveragePrecision()
+    # Add 3 batch updates
+    metric.update([detection], [detection])
+    metric.update([detection], [detection])
+    metric.update([detection], [detection])
+    result = metric.compute()
+
+    # Should be perfect 1.0 mAP across all batches
+    assert abs(result.map50_95 - 1.0) < 1e-6
+
+
+def test_scenario_1_success_case_imperfect_match():
+    """Scenario 1: Success Case with imperfect match"""
+    # Small object (class 0) - area = 30*30 = 900 < 1024
+    small_perfect = Detections(
+        xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64),
+        class_id=np.array([0]),
+        confidence=np.array([0.95]),
+        data={"area": np.array([900])},
+    )
+
+    # Medium object (class 1) - area = 50*50 = 2500 (between 1024 and 9216)
+    medium_target = Detections(
+        xyxy=np.array([[10, 10, 60, 60]], dtype=np.float64),
+        class_id=np.array([1]),
+        data={"area": np.array([2500])},
+    )
+    medium_pred = Detections(
+        xyxy=np.array([[12, 12, 60, 60]], dtype=np.float64),  # Slightly off
+        class_id=np.array([1]),
+        confidence=np.array([0.9]),
+        data={"area": np.array([2304])},  # 48*48
+    )
+
+    # Large objects (classes 0, 1, 2) - area = 100*100 = 10000 > 9216
+    large_targets = Detections(
+        xyxy=np.array(
+            [[10, 10, 110, 110], [120, 120, 220, 220], [230, 230, 330, 330]],
+            dtype=np.float64,
+        ),
+        class_id=np.array([2, 0, 1]),
+        data={"area": np.array([10000, 10000, 10000])},
+    )
+    large_preds = Detections(
+        xyxy=np.array(
+            [[10, 10, 110, 110], [120, 120, 220, 220], [230, 230, 330, 330]],
+            dtype=np.float64,
+        ),
+        class_id=np.array([2, 0, 1]),
+        confidence=np.array([0.9, 0.9, 0.9]),
+        data={"area": np.array([10000, 10000, 10000])},
+    )
+
+    metric = MeanAveragePrecision()
+    metric.update([small_perfect], [small_perfect])
+    metric.update([medium_pred], [medium_target])
+    metric.update([large_preds], [large_targets])
+    result = metric.compute()
+
+    # Should be close to 0.9 (slightly less than perfect due to medium object)
+    assert 0.85 < result.map50_95 < 0.98  # Adjusted upper bound
+    assert result.medium_objects.map50_95 < 1.0  # Medium should be less than perfect
+
+
+def test_scenario_2_missed_detection():
+    """Scenario 2: GT Present, No Prediction (Missed Detection)"""
+    # Small object - area = 30*30 = 900 < 1024
+    small_detection = Detections(
+        xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64),
+        class_id=np.array([0]),
+        confidence=np.array([0.95]),
+        data={"area": np.array([900])},
+    )
+
+    # Medium object - area = 50*50 = 2500 (between 1024 and 9216) - missed
+    medium_target = Detections(
+        xyxy=np.array([[10, 10, 60, 60]], dtype=np.float64),
+        class_id=np.array([1]),
+        data={"area": np.array([2500])},
+    )
+    no_medium_pred = Detections.empty()
+
+    # Large objects - area = 100*100 = 10000 > 9216
+    large_detections = Detections(
+        xyxy=np.array(
+            [[10, 10, 110, 110], [120, 120, 220, 220], [230, 230, 330, 330]],
+            dtype=np.float64,
+        ),
+        class_id=np.array([2, 0, 1]),
+        confidence=np.array([0.9, 0.9, 0.9]),
+        data={"area": np.array([10000, 10000, 10000])},
+    )
+
+    metric = MeanAveragePrecision()
+    metric.update([small_detection], [small_detection])
+    metric.update([no_medium_pred], [medium_target])
+    metric.update([large_detections], [large_detections])
+    result = metric.compute()
+
+    # Medium objects should have 0.0 mAP (missed detection)
+    assert abs(result.medium_objects.map50_95 - 0.0) < 1e-6
+
+
+def test_scenario_3_false_positive():
+    """Scenario 3: No GT, Prediction Present (False Positive)"""
+    # Small object - area = 30*30 = 900 < 1024
+    small_detection = Detections(
+        xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64),
+        class_id=np.array([0]),
+        confidence=np.array([0.95]),
+        data={"area": np.array([900])},
+    )
+
+    # Medium object - area = 50*50 = 2500 - false positive (no GT)
+    medium_pred = Detections(
+        xyxy=np.array([[12, 12, 62, 62]], dtype=np.float64),
+        class_id=np.array([1]),
+        confidence=np.array([0.9]),
+        data={"area": np.array([2500])},
+    )
+    no_medium_target = Detections.empty()
+
+    # Large objects - area = 100*100 = 10000 > 9216
+    large_detections = Detections(
+        xyxy=np.array(
+            [[10, 10, 110, 110], [120, 120, 220, 220], [230, 230, 330, 330]],
+            dtype=np.float64,
+        ),
+        class_id=np.array([2, 0, 1]),
+        confidence=np.array([0.9, 0.9, 0.9]),
+        data={"area": np.array([10000, 10000, 10000])},
+    )
+
+    metric = MeanAveragePrecision()
+    metric.update([small_detection], [small_detection])
+    metric.update([medium_pred], [no_medium_target])
+    metric.update([large_detections], [large_detections])
+    result = metric.compute()
+
+    # Medium objects should have -1 mAP (false positive, matching pycocotools)
+    assert result.medium_objects.map50_95 == -1
+
+
+def test_scenario_4_no_data():
+    """Scenario 4: No GT, No Prediction (Category has no data)"""
+    # Small object - area = 30*30 = 900 < 1024
+    small_detection = Detections(
+        xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64),
+        class_id=np.array([0]),
+        confidence=np.array([0.95]),
+        data={"area": np.array([900])},
+    )
+
+    # Medium object - no data at all
+    no_medium = Detections.empty()
+
+    # Large objects - area = 100*100 = 10000 > 9216 - only classes 0 and 2 (no class 1)
+    large_targets = Detections(
+        xyxy=np.array(
+            [
+                [10, 10, 110, 110],
+                [120, 120, 220, 220],
+            ],
+            dtype=np.float64,
+        ),
+        class_id=np.array([2, 0]),
+        data={"area": np.array([10000, 10000])},
+    )
+    large_preds = Detections(
+        xyxy=np.array(
+            [
+                [10, 10, 110, 110],
+                [120, 120, 220, 220],
+            ],
+            dtype=np.float64,
+        ),
+        class_id=np.array([2, 0]),
+        confidence=np.array([0.9, 0.9]),
+        data={"area": np.array([10000, 10000])},
+    )
+
+    metric = MeanAveragePrecision()
+    metric.update([small_detection], [small_detection])
+    metric.update([no_medium], [no_medium])
+    metric.update([large_preds], [large_targets])
+    result = metric.compute()
+
+    # Should NOT have negative mAP values for overall
+    assert result.map50_95 >= 0.0
+    # Medium objects should have -1 mAP (no data, matching pycocotools)
+    assert result.medium_objects.map50_95 == -1
+
+
+def test_scenario_5_only_one_class_present():
+    """Scenario 5: Only 1 of 3 Classes Present (Perfect Match)"""
+    # Only class 0 objects with perfect matches
+    detections_class_0 = [
+        Detections(
+            xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64),
+            class_id=np.array([0]),
+            confidence=np.array([0.95]),
+        ),
+        Detections(
+            xyxy=np.array([[20, 20, 230, 130]], dtype=np.float64),
+            class_id=np.array([0]),
+            confidence=np.array([0.9]),
+        ),
+    ]
+
+    metric = MeanAveragePrecision()
+    for det in detections_class_0:
+        metric.update([det], [det])
+
+    result = metric.compute()
+
+    # Should be 1.0 mAP (perfect match for the only class present)
+    assert abs(result.map50_95 - 1.0) < 1e-6
+    assert abs(result.map50 - 1.0) < 1e-6
+    assert abs(result.map75 - 1.0) < 1e-6
+
+
+def test_mixed_classes_with_missing_detections():
+    """Test mixed scenario with some classes having no detections"""
+    # Class 0: Perfect detection
+    class_0_det = Detections(
+        xyxy=np.array([[10, 10, 50, 50]], dtype=np.float64),
+        class_id=np.array([0]),
+        confidence=np.array([0.9]),
+    )
+
+    # Class 1: GT exists but no prediction
+    class_1_target = Detections(
+        xyxy=np.array([[60, 60, 100, 100]], dtype=np.float64),
+        class_id=np.array([1]),
+    )
+    class_1_pred = Detections.empty()
+
+    # Class 2: Prediction exists but no GT (false positive)
+    class_2_pred = Detections(
+        xyxy=np.array([[110, 110, 150, 150]], dtype=np.float64),
+        class_id=np.array([2]),
+        confidence=np.array([0.8]),
+    )
+    class_2_target = Detections.empty()
+
+    metric = MeanAveragePrecision()
+    metric.update([class_0_det], [class_0_det])
+    metric.update([class_1_pred], [class_1_target])
+    metric.update([class_2_pred], [class_2_target])
+    result = metric.compute()
+
+    # Should not have negative mAP
+    assert result.map50_95 >= 0.0
+    # Should be less than 1.0 due to missed detection and false positive
+    assert result.map50_95 < 1.0
+
+
+def test_empty_predictions_and_targets():
+    """Test completely empty predictions and targets"""
+    metric = MeanAveragePrecision()
+    metric.update([Detections.empty()], [Detections.empty()])
+    result = metric.compute()
+
+    # Should return -1 for no data (matching pycocotools behavior)
+    assert result.map50_95 == -1
+    assert result.map50 == -1
+    assert result.map75 == -1
+
+    # All object size categories should also be -1
+    assert result.small_objects.map50_95 == -1
+    assert result.medium_objects.map50_95 == -1
+    assert result.large_objects.map50_95 == -1