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Default confidences predict 0.25, val 0.001 (#149)
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@ -39,7 +39,7 @@ dropout: False # use dropout regularization
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val: True # validate/test during training
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val: True # validate/test during training
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save_json: False # save results to JSON file
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save_json: False # save results to JSON file
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save_hybrid: False # save hybrid version of labels (labels + additional predictions)
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save_hybrid: False # save hybrid version of labels (labels + additional predictions)
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conf: 0.001 # object confidence threshold for detection
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conf: null # object confidence threshold for detection (default 0.25 predict, 0.001 val)
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iou: 0.7 # intersection over union (IoU) threshold for NMS
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iou: 0.7 # intersection over union (IoU) threshold for NMS
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max_det: 300 # maximum number of detections per image
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max_det: 300 # maximum number of detections per image
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half: False # use half precision (FP16)
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half: False # use half precision (FP16)
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@ -77,7 +77,8 @@ class BasePredictor:
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name = self.args.name or f"{self.args.mode}"
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name = self.args.name or f"{self.args.mode}"
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self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
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self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
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(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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if self.args.conf is None:
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self.args.conf = 0.25 # default conf=0.25
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self.done_setup = False
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self.done_setup = False
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# Usable if setup is done
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# Usable if setup is done
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@ -65,6 +65,9 @@ class BaseValidator:
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exist_ok=self.args.exist_ok if RANK in {-1, 0} else True)
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exist_ok=self.args.exist_ok if RANK in {-1, 0} else True)
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(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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if self.args.conf is None:
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self.args.conf = 0.001 # default conf=0.001
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self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
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self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
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@smart_inference_mode()
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@smart_inference_mode()
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