Merge branch 'main' into quan

docs/en/modes/predict.md

@@ -385,18 +385,18 @@ Inference arguments:
 
 Visualization arguments:
 
-| Argument      | Type          | Default | Description                                                                                                                                                                   |
-| ------------- | ------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `show`        | `bool`        | `False` | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing.                                           |
-| `save`        | `bool`        | `False` | Enables saving of the annotated images or videos to file. Useful for documentation, further analysis, or sharing results.                                                     |
-| `save_frames` | `bool`        | `False` | When processing videos, saves individual frames as images. Useful for extracting specific frames or for detailed frame-by-frame analysis.                                     |
-| `save_txt`    | `bool`        | `False` | Saves detection results in a text file, following the format `[class] [x_center] [y_center] [width] [height] [confidence]`. Useful for integration with other analysis tools. |
-| `save_conf`   | `bool`        | `False` | Includes confidence scores in the saved text files. Enhances the detail available for post-processing and analysis.                                                           |
-| `save_crop`   | `bool`        | `False` | Saves cropped images of detections. Useful for dataset augmentation, analysis, or creating focused datasets for specific objects.                                             |
-| `show_labels` | `bool`        | `True`  | Displays labels for each detection in the visual output. Provides immediate understanding of detected objects.                                                                |
-| `show_conf`   | `bool`        | `True`  | Displays the confidence score for each detection alongside the label. Gives insight into the model's certainty for each detection.                                            |
-| `show_boxes`  | `bool`        | `True`  | Draws bounding boxes around detected objects. Essential for visual identification and location of objects in images or video frames.                                          |
-| `line_width`  | `None or int` | `None`  | Specifies the line width of bounding boxes. If `None`, the line width is automatically adjusted based on the image size. Provides visual customization for clarity.           |
+| Argument      | Type            | Default           | Description                                                                                                                                                                            |
+| ------------- | --------------- | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `show`        | `bool`          | `False`           | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing.                                                    |
+| `save`        | `bool`          | `False` or `True` | Enables saving of the annotated images or videos to file. Useful for documentation, further analysis, or sharing results. Defaults to True when using CLI & False when used in Python. |
+| `save_frames` | `bool`          | `False`           | When processing videos, saves individual frames as images. Useful for extracting specific frames or for detailed frame-by-frame analysis.                                              |
+| `save_txt`    | `bool`          | `False`           | Saves detection results in a text file, following the format `[class] [x_center] [y_center] [width] [height] [confidence]`. Useful for integration with other analysis tools.          |
+| `save_conf`   | `bool`          | `False`           | Includes confidence scores in the saved text files. Enhances the detail available for post-processing and analysis.                                                                    |
+| `save_crop`   | `bool`          | `False`           | Saves cropped images of detections. Useful for dataset augmentation, analysis, or creating focused datasets for specific objects.                                                      |
+| `show_labels` | `bool`          | `True`            | Displays labels for each detection in the visual output. Provides immediate understanding of detected objects.                                                                         |
+| `show_conf`   | `bool`          | `True`            | Displays the confidence score for each detection alongside the label. Gives insight into the model's certainty for each detection.                                                     |
+| `show_boxes`  | `bool`          | `True`            | Draws bounding boxes around detected objects. Essential for visual identification and location of objects in images or video frames.                                                   |
+| `line_width`  | `None` or `int` | `None`            | Specifies the line width of bounding boxes. If `None`, the line width is automatically adjusted based on the image size. Provides visual customization for clarity.                    |
 
 ## Image and Video Formats
 

docs/en/usage/cfg.md

@@ -171,18 +171,18 @@ Inference arguments:
 
 Visualization arguments:
 
-| Argument      | Type          | Default | Description                                                                                                                                                                   |
-| ------------- | ------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `show`        | `bool`        | `False` | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing.                                           |
-| `save`        | `bool`        | `False` | Enables saving of the annotated images or videos to file. Useful for documentation, further analysis, or sharing results.                                                     |
-| `save_frames` | `bool`        | `False` | When processing videos, saves individual frames as images. Useful for extracting specific frames or for detailed frame-by-frame analysis.                                     |
-| `save_txt`    | `bool`        | `False` | Saves detection results in a text file, following the format `[class] [x_center] [y_center] [width] [height] [confidence]`. Useful for integration with other analysis tools. |
-| `save_conf`   | `bool`        | `False` | Includes confidence scores in the saved text files. Enhances the detail available for post-processing and analysis.                                                           |
-| `save_crop`   | `bool`        | `False` | Saves cropped images of detections. Useful for dataset augmentation, analysis, or creating focused datasets for specific objects.                                             |
-| `show_labels` | `bool`        | `True`  | Displays labels for each detection in the visual output. Provides immediate understanding of detected objects.                                                                |
-| `show_conf`   | `bool`        | `True`  | Displays the confidence score for each detection alongside the label. Gives insight into the model's certainty for each detection.                                            |
-| `show_boxes`  | `bool`        | `True`  | Draws bounding boxes around detected objects. Essential for visual identification and location of objects in images or video frames.                                          |
-| `line_width`  | `None or int` | `None`  | Specifies the line width of bounding boxes. If `None`, the line width is automatically adjusted based on the image size. Provides visual customization for clarity.           |
+| Argument      | Type            | Default           | Description                                                                                                                                                                            |
+| ------------- | --------------- | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `show`        | `bool`          | `False`           | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing.                                                    |
+| `save`        | `bool`          | `False` or `True` | Enables saving of the annotated images or videos to file. Useful for documentation, further analysis, or sharing results. Defaults to True when using CLI & False when used in Python. |
+| `save_frames` | `bool`          | `False`           | When processing videos, saves individual frames as images. Useful for extracting specific frames or for detailed frame-by-frame analysis.                                              |
+| `save_txt`    | `bool`          | `False`           | Saves detection results in a text file, following the format `[class] [x_center] [y_center] [width] [height] [confidence]`. Useful for integration with other analysis tools.          |
+| `save_conf`   | `bool`          | `False`           | Includes confidence scores in the saved text files. Enhances the detail available for post-processing and analysis.                                                                    |
+| `save_crop`   | `bool`          | `False`           | Saves cropped images of detections. Useful for dataset augmentation, analysis, or creating focused datasets for specific objects.                                                      |
+| `show_labels` | `bool`          | `True`            | Displays labels for each detection in the visual output. Provides immediate understanding of detected objects.                                                                         |
+| `show_conf`   | `bool`          | `True`            | Displays the confidence score for each detection alongside the label. Gives insight into the model's certainty for each detection.                                                     |
+| `show_boxes`  | `bool`          | `True`            | Draws bounding boxes around detected objects. Essential for visual identification and location of objects in images or video frames.                                                   |
+| `line_width`  | `None` or `int` | `None`            | Specifies the line width of bounding boxes. If `None`, the line width is automatically adjusted based on the image size. Provides visual customization for clarity.                    |
 
 [Predict Guide](../modes/predict.md){ .md-button }
 

ultralytics/__init__.py

@@ -1,6 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.89"
+__version__ = "8.2.90"
+
 
 import os
 

ultralytics/engine/exporter.py

@@ -411,7 +411,7 @@ class Exporter:
         """YOLOv8 ONNX export."""
         requirements = ["onnx>=1.12.0"]
         if self.args.simplify:
-            requirements += ["onnxslim>=0.1.31", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")]
+            requirements += ["onnxslim==0.1.32", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")]
         check_requirements(requirements)
         import onnx  # noqa
 

ultralytics/engine/trainer.py

@@ -28,6 +28,7 @@ from ultralytics.utils import (
     DEFAULT_CFG,
     LOCAL_RANK,
     LOGGER,
+    MACOS,
     RANK,
     TQDM,
     __version__,
@@ -453,7 +454,10 @@ class BaseTrainer:
                 self.stop |= epoch >= self.epochs  # stop if exceeded epochs
             self.run_callbacks("on_fit_epoch_end")
             gc.collect()
-            torch.cuda.empty_cache()  # clear GPU memory at end of epoch, may help reduce CUDA out of memory errors
+            if MACOS:
+                torch.mps.empty_cache()  # clear unified memory at end of epoch, may help MPS' management of 'unlimited' virtual memoy
+            else:
+                torch.cuda.empty_cache()  # clear GPU memory at end of epoch, may help reduce CUDA out of memory errors
 
             # Early Stopping
             if RANK != -1:  # if DDP training
@@ -475,7 +479,11 @@ class BaseTrainer:
                 self.plot_metrics()
             self.run_callbacks("on_train_end")
         gc.collect()
-        torch.cuda.empty_cache()
+        if MACOS:
+            torch.mps.empty_cache()
+        else:
+            torch.cuda.empty_cache()
+
         self.run_callbacks("teardown")
 
     def read_results_csv(self):