Add detection_class_name to targets protocol

src/batdetect2/data/annotations/batdetect2.py

@@ -301,7 +301,8 @@ def load_batdetect2_merged_annotated_dataset(
     for ann in content:
         try:
             ann = FileAnnotation.model_validate(ann)
-        except ValueError:
+        except ValueError as err:
+            logger.warning(f"Invalid annotation file: {err}")
             continue
 
         if (
@@ -309,14 +310,17 @@ def load_batdetect2_merged_annotated_dataset(
             and dataset.filter.only_annotated
             and not ann.annotated
         ):
+            logger.debug(f"Skipping incomplete annotation {ann.id}")
             continue
 
         if dataset.filter and dataset.filter.exclude_issues and ann.issues:
+            logger.debug(f"Skipping annotation with issues {ann.id}")
             continue
 
         try:
             clip = file_annotation_to_clip(ann, audio_dir=audio_dir)
-        except FileNotFoundError:
+        except FileNotFoundError as err:
+            logger.warning(f"Error loading annotations: {err}")
             continue
 
         annotations.append(file_annotation_to_clip_annotation(ann, clip))

src/batdetect2/data/summary.py

@@ -100,8 +100,11 @@ def extract_sound_events_df(
 
             class_name = targets.encode_class(sound_event)
 
-            if class_name is None and exclude_generic:
+            if class_name is None:
+                if exclude_generic:
                     continue
+                else:
+                    class_name = targets.detection_class_name
 
             start_time, low_freq, end_time, high_freq = compute_bounds(
                 sound_event.sound_event.geometry
@@ -153,7 +156,7 @@ def compute_class_summary(
     sound_events = extract_sound_events_df(
         dataset,
         targets,
-        exclude_generic=True,
+        exclude_generic=False,
         exclude_non_target=True,
     )
     recordings = extract_recordings_df(dataset)

src/batdetect2/postprocess/decoding.py

@@ -103,7 +103,7 @@ def convert_raw_prediction_to_sound_event_prediction(
     tags = [
         *get_generic_tags(
             raw_prediction.detection_score,
-            generic_class_tags=targets.generic_class_tags,
+            generic_class_tags=targets.detection_class_tags,
         ),
         *get_class_tags(
             raw_prediction.class_scores,

src/batdetect2/targets/__init__.py

@@ -140,16 +140,18 @@ class Targets(TargetProtocol):
     """
 
     class_names: List[str]
-    generic_class_tags: List[data.Tag]
+    detection_class_tags: List[data.Tag]
     dimension_names: List[str]
+    detection_class_name: str
 
     def __init__(
         self,
+        detection_class_name: str,
         encode_fn: SoundEventEncoder,
         decode_fn: SoundEventDecoder,
         roi_mapper: ROITargetMapper,
         class_names: list[str],
-        generic_class_tags: List[data.Tag],
+        detection_class_tags: List[data.Tag],
         filter_fn: Optional[SoundEventCondition] = None,
         roi_mapper_overrides: Optional[dict[str, ROITargetMapper]] = None,
     ):
@@ -175,8 +177,9 @@ class Targets(TargetProtocol):
         transform_fn : SoundEventTransformation, optional
             Configured function to transform annotation tags. Defaults to None.
         """
+        self.detection_class_name = detection_class_name
         self.class_names = class_names
-        self.generic_class_tags = generic_class_tags
+        self.detection_class_tags = detection_class_tags
         self.dimension_names = roi_mapper.dimension_names
 
         self._roi_mapper = roi_mapper
@@ -381,7 +384,8 @@ def build_targets(config: Optional[TargetConfig] = None) -> Targets:
         decode_fn=decode_fn,
         class_names=class_names,
         roi_mapper=roi_mapper,
-        generic_class_tags=generic_class_tags,
+        detection_class_name=config.detection_target.name,
+        detection_class_tags=generic_class_tags,
         roi_mapper_overrides=roi_overrides,
     )
 

src/batdetect2/train/train.py

@@ -158,25 +158,13 @@ def build_trainer_callbacks(
     if run_name is not None:
         checkpoint_dir = checkpoint_dir / run_name
 
-    filename = "best-{epoch:02d}-{val_loss:.0f}"
-
-    if run_name is not None:
-        filename = f"run_{run_name}_{filename}"
-
-    if experiment_name is not None:
-        filename = f"experiment_{experiment_name}_{filename}"
-
-    model_checkpoint = ModelCheckpoint(
+    return [
+        ModelCheckpoint(
             dirpath=str(checkpoint_dir),
             save_top_k=1,
-        filename=filename,
+            filename="best-{epoch:02d}-{val_loss:.0f}",
             monitor="total_loss/val",
-    )
-
-    model_checkpoint.CHECKPOINT_EQUALS_CHAR = "_"  # type: ignore
-
-    return [
-        model_checkpoint,
+        ),
         ValidationMetrics(
             metrics=[
                 DetectionAveragePrecision(),
@@ -226,7 +214,8 @@ def build_trainer(
             config=conf.evaluation,
             preprocessor=build_preprocessor(conf.preprocess),
             checkpoint_dir=checkpoint_dir,
-            experiment_name=train_logger.name,
+            experiment_name=experiment_name,
+            run_name=run_name,
         ),
     )
 

src/batdetect2/typing/targets.py

@@ -94,8 +94,10 @@ class TargetProtocol(Protocol):
     class_names: List[str]
     """Ordered list of unique names for the specific target classes."""
 
-    generic_class_tags: List[data.Tag]
-    """List of tags representing the generic (unclassified) category."""
+    detection_class_tags: List[data.Tag]
+    """List of tags representing the detection category (unclassified)."""
+
+    detection_class_name: str
 
     dimension_names: List[str]
     """Names of the size dimensions (e.g., ['width', 'height'])."""