Rename threshold to conf_threshold for clarity

cvat-cli/src/cvat_cli/_internal/commands.py

@@ -470,7 +470,7 @@ def configure_parser(self, parser: argparse.ArgumentParser) -> None:
         )
 
         parser.add_argument(
-            "--threshold",
+            "--conf-threshold",
             type=parse_threshold,
             help="Confidence threshold for filtering detections",
             default=None,
@@ -486,7 +486,7 @@ def execute(
         function_parameters: dict[str, Any],
         clear_existing: bool = False,
         allow_unmatched_labels: bool = False,
-        threshold: Optional[float],
+        conf_threshold: Optional[float],
     ) -> None:
         if function_module is not None:
             function = importlib.import_module(function_module)
@@ -511,5 +511,5 @@ def execute(
             pbar=DeferredTqdmProgressReporter(),
             clear_existing=clear_existing,
             allow_unmatched_labels=allow_unmatched_labels,
-            threshold=threshold,
+            conf_threshold=conf_threshold,
         )

cvat-sdk/cvat_sdk/auto_annotation/driver.py

@@ -223,7 +223,7 @@ def validate_and_remap(self, shapes: list[models.LabeledShapeRequest], ds_frame:
 @attrs.frozen(kw_only=True)
 class _DetectionFunctionContextImpl(DetectionFunctionContext):
     frame_name: str
-    threshold: Optional[float] = None
+    conf_threshold: Optional[float] = None
 
 
 def annotate_task(
@@ -234,7 +234,7 @@ def annotate_task(
     pbar: Optional[ProgressReporter] = None,
     clear_existing: bool = False,
     allow_unmatched_labels: bool = False,
-    threshold: Optional[float] = None,
+    conf_threshold: Optional[float] = None,
 ) -> None:
     """
     Downloads data for the task with the given ID, applies the given function to it
@@ -267,15 +267,15 @@ def annotate_task(
     If it's set to true, then such labels are allowed, and any annotations returned by the
     function that refer to this label are ignored. Otherwise, BadFunctionError is raised.
 
-    The threshold parameter must be None or a number between 0 and 1. It will be passed
-    to the function as the threshold attribute of the context object.
+    The conf_threshold parameter must be None or a number between 0 and 1. It will be passed
+    to the function as the conf_threshold attribute of the context object.
     """
 
     if pbar is None:
         pbar = NullProgressReporter()
 
-    if threshold is not None and not 0 <= threshold <= 1:
-        raise ValueError("threshold must be None or a number between 0 and 1")
+    if conf_threshold is not None and not 0 <= conf_threshold <= 1:
+        raise ValueError("conf_threshold must be None or a number between 0 and 1")
 
     dataset = TaskDataset(client, task_id, load_annotations=False)
 
@@ -293,7 +293,7 @@ def annotate_task(
     with pbar.task(total=len(dataset.samples), unit="samples"):
         for sample in pbar.iter(dataset.samples):
             frame_shapes = function.detect(
-                _DetectionFunctionContextImpl(frame_name=sample.frame_name, threshold=threshold),
+                _DetectionFunctionContextImpl(frame_name=sample.frame_name, conf_threshold=conf_threshold),
                 sample.media.load_image(),
             )
             mapper.validate_and_remap(frame_shapes, sample.frame_index)

cvat-sdk/cvat_sdk/auto_annotation/functions/torchvision_detection.py

@@ -30,14 +30,14 @@ def spec(self) -> cvataa.DetectionFunctionSpec:
     def detect(
         self, context: cvataa.DetectionFunctionContext, image: PIL.Image.Image
     ) -> list[models.LabeledShapeRequest]:
-        threshold = context.threshold or 0
+        conf_threshold = context.conf_threshold or 0
         results = self._model([self._transforms(image)])
 
         return [
             cvataa.rectangle(label.item(), [x.item() for x in box])
             for result in results
             for box, label, score in zip(result["boxes"], result["labels"], result["scores"])
-            if score >= threshold
+            if score >= conf_threshold
         ]
 
 

cvat-sdk/cvat_sdk/auto_annotation/functions/torchvision_keypoint_detection.py

@@ -38,7 +38,7 @@ def spec(self) -> cvataa.DetectionFunctionSpec:
     def detect(
         self, context: cvataa.DetectionFunctionContext, image: PIL.Image.Image
     ) -> list[models.LabeledShapeRequest]:
-        threshold = context.threshold or 0
+        conf_threshold = context.conf_threshold or 0
         results = self._model([self._transforms(image)])
 
         return [
@@ -57,7 +57,7 @@ def detect(
             for keypoints, label, score in zip(
                 result["keypoints"], result["labels"], result["scores"]
             )
-            if score >= threshold
+            if score >= conf_threshold
         ]
 
 

cvat-sdk/cvat_sdk/auto_annotation/interface.py

@@ -53,7 +53,7 @@ def frame_name(self) -> str:
 
     @property
     @abc.abstractmethod
-    def threshold(self) -> Optional[float]:
+    def conf_threshold(self) -> Optional[float]:
         """
         The confidence threshold that the function should use for filtering
         detections.

site/content/en/docs/api_sdk/sdk/auto-annotation.md

@@ -72,7 +72,7 @@ class TorchvisionDetectionFunction:
         self, context: cvataa.DetectionFunctionContext, image: PIL.Image.Image
     ) -> list[models.LabeledShapeRequest]:
         # determine the threshold for filtering results
-        threshold = context.threshold or 0
+        conf_threshold = context.conf_threshold or 0
 
         # convert the input into a form the model can understand
         transformed_image = [self._transforms(image)]
@@ -85,7 +85,7 @@ class TorchvisionDetectionFunction:
             cvataa.rectangle(label.item(), [x.item() for x in box])
             for result in results
             for box, label, score in zip(result["boxes"], result["labels"], result["scores"])
-            if score >= threshold
+            if score >= conf_threshold
         ]
 
 # log into the CVAT server
@@ -122,7 +122,7 @@ that these objects must follow.
   The following fields are available:
 
   - `frame_name` (`str`). The file name of the frame on the CVAT server.
-  - `threshold` (`float | None`). The confidence threshold that the function
+  - `conf_threshold` (`float | None`). The confidence threshold that the function
     should use to filter objects. If `None`, the function may apply a default
     threshold at its discretion.
 
@@ -206,7 +206,7 @@ and any shapes referring to them will be dropped.
 Same logic applies to sub-label IDs.
 
 It's possible to pass a custom confidence threshold to the function via the
-`threshold` parameter.
+`conf_threshold` parameter.
 
 `annotate_task` will raise a `BadFunctionError` exception
 if it detects that the function violated the AA function protocol.