ultralytics 8.0.97 confusion matrix, windows, docs updates (#2511)

Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Dowon <ks2515@naver.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

docs/datasets/pose/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn how to format your dataset for training YOLO models with Ultralytics YOLO format using our concise tutorial and example YAML files.
 ---
 
 # Pose Estimation Datasets Overview
@@ -15,26 +16,26 @@ The dataset format used for training YOLO segmentation models is as follows:
 1. One text file per image: Each image in the dataset has a corresponding text file with the same name as the image file and the ".txt" extension.
 2. One row per object: Each row in the text file corresponds to one object instance in the image.
 3. Object information per row: Each row contains the following information about the object instance:
-   - Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
-   - Object center coordinates: The x and y coordinates of the center of the object, normalized to be between 0 and 1.
-   - Object width and height: The width and height of the object, normalized to be between 0 and 1.
-   - Object keypoint coordinates: The keypoints of the object, normalized to be between 0 and 1.
+    - Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
+    - Object center coordinates: The x and y coordinates of the center of the object, normalized to be between 0 and 1.
+    - Object width and height: The width and height of the object, normalized to be between 0 and 1.
+    - Object keypoint coordinates: The keypoints of the object, normalized to be between 0 and 1.
 
 Here is an example of the label format for pose estimation task:
 
 Format with Dim = 2
 
 ```
-<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2>  <pxn> <pyn>
+<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn>
 ```
+
 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 boudning box, and `<px1> <py1> <px2> <py2> ... <pxn> <pyn>` are the pixel coordinates of the keypoints. The coordinates are separated by spaces.
 
 ** Dataset file format **
 
@@ -62,6 +63,7 @@ The `names` field is a list of the names of the object classes. The order of the
 NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory
 
 Alternatively, you can directly define class names like this:
+
 ```
 names:
   0: person
@@ -69,7 +71,7 @@ names:
 ```
 
 (Optional) if the points are symmetric then need flip_idx, like left-right side of human or face.
-For example let's say there're five keypoints of facial landmark: [left eye, right eye, nose, left point of mouth, right point of mouse], and the original index is [0, 1, 2, 3, 4], then flip_idx is [1, 0, 2, 4, 3].(just exchange the left-right index, i.e 0-1 and 3-4, and do not modify others like nose in this example) 
+For example let's say there're five keypoints of facial landmark: [left eye, right eye, nose, left point of mouth, right point of mouse], and the original index is [0, 1, 2, 3, 4], then flip_idx is [1, 0, 2, 4, 3].(just exchange the left-right index, i.e 0-1 and 3-4, and do not modify others like nose in this example)
 
 ** Example **
 
@@ -86,6 +88,7 @@ flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
 ```
 
 ## Usage
+
 !!! example ""
 
     === "Python"
@@ -107,6 +110,7 @@ flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
         ```
 
 ## Supported Datasets
+
 TODO
 
 ## Port or Convert label formats
@@ -117,4 +121,4 @@ TODO
 from ultralytics.yolo.data.converter import convert_coco
 
 convert_coco(labels_dir='../coco/annotations/', use_keypoints=True)
-```
+```