Default confidences predict 0.25, val 0.001 (#149)

ultralytics/yolo/configs/default.yaml

@@ -39,7 +39,7 @@ dropout: False # use dropout regularization
 val: True # validate/test during training
 save_json: False # save results to JSON file
 save_hybrid: False # save hybrid version of labels (labels + additional predictions)
-conf: 0.001 # object confidence threshold for detection
+conf: null # object confidence threshold for detection (default 0.25 predict, 0.001 val)
 iou: 0.7 # intersection over union (IoU) threshold for NMS
 max_det: 300 # maximum number of detections per image
 half: False # use half precision (FP16)

ultralytics/yolo/engine/predictor.py

@@ -77,7 +77,8 @@ class BasePredictor:
         name = self.args.name or f"{self.args.mode}"
         self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
         (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
-
+        if self.args.conf is None:
+            self.args.conf = 0.25  # default conf=0.25
         self.done_setup = False
 
         # Usable if setup is done

ultralytics/yolo/engine/validator.py

@@ -65,6 +65,9 @@ class BaseValidator:
                                                    exist_ok=self.args.exist_ok if RANK in {-1, 0} else True)
         (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
 
+        if self.args.conf is None:
+            self.args.conf = 0.001  # default conf=0.001
+
         self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()})  # add callbacks
 
     @smart_inference_mode()