New ASSETS and trackers GMC cleanup (#4425)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/engine/model.py

@@ -9,8 +9,8 @@ from ultralytics.cfg import get_cfg
 from ultralytics.engine.exporter import Exporter
 from ultralytics.hub.utils import HUB_WEB_ROOT
 from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load
-from ultralytics.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks, emojis,
-                               is_git_dir, yaml_load)
+from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, callbacks, emojis,
+                               yaml_load)
 from ultralytics.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
 from ultralytics.utils.downloads import GITHUB_ASSET_STEMS
 from ultralytics.utils.torch_utils import smart_inference_mode
@@ -218,7 +218,7 @@ class Model:
             (List[ultralytics.engine.results.Results]): The prediction results.
         """
         if source is None:
-            source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
+            source = ASSETS
             LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
         is_cli = (sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')) and any(
             x in sys.argv for x in ('predict', 'track', 'mode=predict', 'mode=track'))
@@ -390,6 +390,7 @@ class Model:
         """
         self._check_is_pytorch_model()
         self.model.to(device)
+        return self
 
     def tune(self, *args, **kwargs):
         """

ultralytics/engine/predictor.py

@@ -47,7 +47,7 @@ STREAM_WARNING = """
     WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed,
     causing potential out-of-memory errors for large sources or long-running streams/videos.
 
-    Usage:
+    Example:
         results = model(source=..., stream=True)  # generator of Results objects
         for r in results:
             boxes = r.boxes  # Boxes object for bbox outputs