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.