ultralytics 8.0.196 instance-mean Segment loss (#5285)

Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>

docs/datasets/detect/coco8.md

@@ -8,11 +8,7 @@ keywords: Ultralytics, COCO8 dataset, object detection, model testing, dataset c
 
 ## Introduction
 
-[Ultralytics](https://ultralytics.com) COCO8 is a small, but versatile object detection dataset composed of the first 8
-images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging
-object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be
-easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training
-larger datasets.
+[Ultralytics](https://ultralytics.com) COCO8 is a small, but versatile object detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
 This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
 and [YOLOv8](https://github.com/ultralytics/ultralytics).

docs/datasets/detect/index.md

@@ -99,7 +99,7 @@ You can easily convert labels from the popular COCO dataset format to the YOLO f
 
         ```python
         from ultralytics.data.converter import convert_coco
-        
+
         convert_coco(labels_dir='path/to/coco/annotations/')
         ```
 

docs/datasets/detect/open-images-v7.md

@@ -53,9 +53,9 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an im
 !!! warning
 
     The complete Open Images V7 dataset comprises 1,743,042 training images and 41,620 validation images, requiring approximately **561 GB of storage space** upon download.
-    
+
     Executing the commands provided below will trigger an automatic download of the full dataset if it's not already present locally. Before running the below example it's crucial to:
-    
+
     - Verify that your device has enough storage capacity.
     - Ensure a robust and speedy internet connection.
 

docs/datasets/index.md

@@ -120,4 +120,4 @@ Contributing a new dataset involves several steps to ensure that it aligns well
     zip_directory(path)
     ```
 
-By following these steps, you can contribute a new dataset that integrates well with Ultralytics' existing structure.
+By following these steps, you can contribute a new dataset that integrates well with Ultralytics' existing structure.

docs/datasets/obb/dota-v2.md

@@ -126,4 +126,4 @@ For those leveraging DOTA v2 in their endeavors, it's pertinent to cite the rele
         }
         ```
 
-A special note of gratitude to the team behind DOTA v2 for their commendable effort in curating this dataset. For an exhaustive understanding of the dataset and its nuances, please visit the [official DOTA v2 website](https://captain-whu.github.io/DOTA/index.html).
+A special note of gratitude to the team behind DOTA v2 for their commendable effort in curating this dataset. For an exhaustive understanding of the dataset and its nuances, please visit the [official DOTA v2 website](https://captain-whu.github.io/DOTA/index.html).

docs/datasets/obb/index.md

@@ -75,7 +75,7 @@ Transitioning labels from the DOTA dataset format to the YOLO OBB format can be
 
         ```python
         from ultralytics.data.converter import convert_dota_to_yolo_obb
-        
+
         convert_dota_to_yolo_obb('path/to/DOTA')
         ```
 

docs/datasets/pose/coco8-pose.md

@@ -8,11 +8,7 @@ keywords: Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, mode
 
 ## Introduction
 
-[Ultralytics](https://ultralytics.com) COCO8-Pose is a small, but versatile pose detection dataset composed of the first
-8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and
-debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough
-to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before
-training larger datasets.
+[Ultralytics](https://ultralytics.com) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
 This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
 and [YOLOv8](https://github.com/ultralytics/ultralytics).

docs/datasets/pose/index.md

@@ -34,7 +34,7 @@ Format with Dim = 3
 <class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> <px2> <py2> <p2-visibility> <pxn> <pyn> <p2-visibility>
 ```
 
-In this format, `<class-index>` is the index of the class for the object,`<x> <y> <width> <height>` are coordinates of boudning box, and `<px1> <py1> <px2> <py2> ... <pxn> <pyn>` are the pixel coordinates of the keypoints. The coordinates are separated by spaces.
+In this format, `<class-index>` is the index of the class for the object,`<x> <y> <width> <height>` are coordinates of bounding box, and `<px1> <py1> <px2> <py2> ... <pxn> <pyn>` are the pixel coordinates of the keypoints. The coordinates are separated by spaces.
 
 ### Dataset YAML format
 
@@ -131,7 +131,7 @@ Ultralytics provides a convenient conversion tool to convert labels from the pop
 
         ```python
         from ultralytics.data.converter import convert_coco
-        
+
         convert_coco(labels_dir='path/to/coco/annotations/', use_keypoints=True)
         ```
 

docs/datasets/segment/coco8-seg.md

@@ -8,11 +8,7 @@ keywords: COCO8-Seg dataset, Ultralytics, YOLOv8, instance segmentation, dataset
 
 ## Introduction
 
-[Ultralytics](https://ultralytics.com) COCO8-Seg is a small, but versatile instance segmentation dataset composed of the
-first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and
-debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to
-be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training
-larger datasets.
+[Ultralytics](https://ultralytics.com) COCO8-Seg is a small, but versatile instance segmentation dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
 This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
 and [YOLOv8](https://github.com/ultralytics/ultralytics).

docs/datasets/segment/index.md

@@ -107,7 +107,7 @@ You can easily convert labels from the popular COCO dataset format to the YOLO f
 
         ```python
         from ultralytics.data.converter import convert_coco
-        
+
         convert_coco(labels_dir='path/to/coco/annotations/', use_segments=True)
         ```
 
@@ -129,7 +129,7 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
 
         ```python
         from ultralytics.data.annotator import auto_annotate
-         
+
         auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
         ```
 

docs/datasets/track/index.md

@@ -8,8 +8,7 @@ keywords: Ultralytics, YOLO, multi-object tracking, datasets, detection, segment
 
 ## Dataset Format (Coming Soon)
 
-Multi-Object Detector doesn't need standalone training and directly supports pre-trained detection, segmentation or Pose models.
-Support for training trackers alone is coming soon
+Multi-Object Detector doesn't need standalone training and directly supports pre-trained detection, segmentation or Pose models. Support for training trackers alone is coming soon
 
 ## Usage