diff --git a/src/batdetect2/data/predictions/raw.py b/src/batdetect2/data/predictions/raw.py
index 08cd3d3..814784d 100644
--- a/src/batdetect2/data/predictions/raw.py
+++ b/src/batdetect2/data/predictions/raw.py
@@ -194,10 +194,10 @@ class RawFormatter(OutputFormatterProtocol[BatDetect2Prediction]):
                     detection_data.geometry.item()
                 )
             else:
-                start_time = detection_data.start_time
-                end_time = detection_data.end_time
-                low_freq = detection_data.low_freq
-                high_freq = detection_data.high_freq
+                start_time = detection_data.start_time.item()
+                end_time = detection_data.end_time.item()
+                low_freq = detection_data.low_freq.item()
+                high_freq = detection_data.high_freq.item()
                 geometry = data.BoundingBox.model_construct(
                     coordinates=[start_time, low_freq, end_time, high_freq]
                 )
diff --git a/src/batdetect2/evaluate/metrics/classification.py b/src/batdetect2/evaluate/metrics/classification.py
index d55d27b..c18a9b1 100644
--- a/src/batdetect2/evaluate/metrics/classification.py
+++ b/src/batdetect2/evaluate/metrics/classification.py
@@ -29,6 +29,7 @@ __all__ = [
     "ClassificationMetric",
     "ClassificationMetricConfig",
     "build_classification_metric",
+    "compute_precision_recall_curves",
 ]
 
 
diff --git a/src/batdetect2/evaluate/metrics/common.py b/src/batdetect2/evaluate/metrics/common.py
index 0aa632d..dfc47bc 100644
--- a/src/batdetect2/evaluate/metrics/common.py
+++ b/src/batdetect2/evaluate/metrics/common.py
@@ -52,6 +52,14 @@ def average_precision(
         num_positives=num_positives,
     )
 
+    # pascal 12 way
+    return _average_precision(recall, precision)
+
+
+def _average_precision(
+    recall: np.ndarray,
+    precision: np.ndarray,
+) -> float:
     # pascal 12 way
     mprec = np.hstack((0, precision, 0))
     mrec = np.hstack((0, recall, 1))
@@ -59,5 +67,4 @@ def average_precision(
         mprec[ii] = np.maximum(mprec[ii], mprec[ii + 1])
     inds = np.where(np.not_equal(mrec[1:], mrec[:-1]))[0] + 1
     ave_prec = ((mrec[inds] - mrec[inds - 1]) * mprec[inds]).sum()
-
     return ave_prec
diff --git a/src/batdetect2/evaluate/metrics/top_class.py b/src/batdetect2/evaluate/metrics/top_class.py
index 16b0fd7..d16575b 100644
--- a/src/batdetect2/evaluate/metrics/top_class.py
+++ b/src/batdetect2/evaluate/metrics/top_class.py
@@ -1,3 +1,4 @@
+from collections import defaultdict
 from dataclasses import dataclass
 from typing import (
     Annotated,
diff --git a/src/batdetect2/evaluate/tasks/__init__.py b/src/batdetect2/evaluate/tasks/__init__.py
index 4b62c16..5f4947d 100644
--- a/src/batdetect2/evaluate/tasks/__init__.py
+++ b/src/batdetect2/evaluate/tasks/__init__.py
@@ -1,8 +1,9 @@
-from typing import Annotated, Optional, Union
+from typing import Annotated, Optional, Sequence, Union
 
 from pydantic import Field
+from soundevent import data
 
-from batdetect2.evaluate.tasks.base import tasks_registry
+from batdetect2.evaluate.tasks.base import BaseTaskConfig, tasks_registry
 from batdetect2.evaluate.tasks.classification import ClassificationTaskConfig
 from batdetect2.evaluate.tasks.clip_classification import (
     ClipClassificationTaskConfig,
@@ -11,11 +12,16 @@ from batdetect2.evaluate.tasks.clip_detection import ClipDetectionTaskConfig
 from batdetect2.evaluate.tasks.detection import DetectionTaskConfig
 from batdetect2.evaluate.tasks.top_class import TopClassDetectionTaskConfig
 from batdetect2.targets import build_targets
-from batdetect2.typing import EvaluatorProtocol, TargetProtocol
+from batdetect2.typing import (
+    BatDetect2Prediction,
+    EvaluatorProtocol,
+    TargetProtocol,
+)
 
 __all__ = [
     "TaskConfig",
     "build_task",
+    "evaluate_task",
 ]
 
 
@@ -37,3 +43,25 @@ def build_task(
 ) -> EvaluatorProtocol:
     targets = targets or build_targets()
     return tasks_registry.build(config, targets)
+
+
+def evaluate_task(
+    clip_annotations: Sequence[data.ClipAnnotation],
+    predictions: Sequence[BatDetect2Prediction],
+    task: Optional["str"] = None,
+    targets: Optional[TargetProtocol] = None,
+    config: Optional[Union[TaskConfig, dict]] = None,
+):
+    if isinstance(config, BaseTaskConfig):
+        task_obj = build_task(config, targets)
+        return task_obj.evaluate(clip_annotations, predictions)
+
+    if task is None:
+        raise ValueError(
+            "Task must be specified if a full config is not provided.",
+        )
+
+    config_class = tasks_registry.get_config_type(task)
+    config = config_class.model_validate(config or {})  # type: ignore
+    task_obj = build_task(config, targets)  # type: ignore
+    return task_obj.evaluate(clip_annotations, predictions)