Release 8.0.5 PR (#279)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Izam Mohammed <106471909+izam-mohammed@users.noreply.github.com>
Co-authored-by: Yue WANG 王跃 <92371174+yuewangg@users.noreply.github.com>
Co-authored-by: Thibaut Lucas <thibautlucas13@gmail.com>

ultralytics/yolo/engine/exporter.py

@@ -177,6 +177,7 @@ class Exporter:
         for p in model.parameters():
             p.requires_grad = False
         model.eval()
+        model.float()
         model = model.fuse()
         for k, m in model.named_modules():
             if isinstance(m, (Detect, Segment)):

ultralytics/yolo/engine/model.py

@@ -111,7 +111,7 @@ class YOLO:
         self.model.fuse()
 
     @smart_inference_mode()
-    def predict(self, source, return_outputs=True, **kwargs):
+    def predict(self, source, return_outputs=False, **kwargs):
         """
         Visualize prediction.
 
@@ -191,6 +191,9 @@ class YOLO:
             self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
             self.model = self.trainer.model
         self.trainer.train()
+        # update model and configs after training
+        self.model, _ = attempt_load_one_weight(str(self.trainer.best))
+        self.overrides = self.model.args
 
     def to(self, device):
         """

ultralytics/yolo/engine/predictor.py

@@ -105,7 +105,7 @@ class BasePredictor:
     def postprocess(self, preds, img, orig_img):
         return preds
 
-    def setup(self, source=None, model=None, return_outputs=True):
+    def setup(self, source=None, model=None, return_outputs=False):
         # source
         source = str(source if source is not None else self.args.source)
         is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
@@ -161,7 +161,7 @@ class BasePredictor:
         return model
 
     @smart_inference_mode()
-    def __call__(self, source=None, model=None, return_outputs=True):
+    def __call__(self, source=None, model=None, return_outputs=False):
         self.run_callbacks("on_predict_start")
         model = self.model if self.done_setup else self.setup(source, model, return_outputs)
         model.eval()