Update Pose docs with keypoint explanations (#14248)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/integrations/google-colab.md

@@ -138,7 +138,7 @@ For more tips on managing your Colab session, visit the [Google Colab FAQ page](
 
 ### Can I use custom datasets for training YOLOv8 models in Google Colab?
 
-Yes, you can use custom datasets to train YOLOv8 models in Google Colab. Upload your dataset to Google Drive and load it directly into your Colab notebook. You can follow Nicolai's YouTube guide, [How to Train YOLOv8 Models on Your Custom Dataset](https://www.youtube.com/watch?v=LNwODJXcvt4?si=lB9UAc4hatSSEr2a), or refer to the [Custom Dataset Training guide](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab) for detailed steps.
+Yes, you can use custom datasets to train YOLOv8 models in Google Colab. Upload your dataset to Google Drive and load it directly into your Colab notebook. You can follow Nicolai's YouTube guide, [How to Train YOLOv8 Models on Your Custom Dataset](https://www.youtube.com/watch?v=LNwODJXcvt4), or refer to the [Custom Dataset Training guide](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab) for detailed steps.
 
 ### What should I do if my Google Colab training session is interrupted?
 

docs/en/tasks/pose.md

@@ -39,6 +39,26 @@ The output of a pose estimation model is a set of points that represent the keyp
 
     YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt`. These models are trained on the [COCO keypoints](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml) dataset and are suitable for a variety of pose estimation tasks.
 
+    In the default YOLOv8 pose model, there are 17 keypoints, each representing a different part of the human body. Here is the mapping of each index to its respective body joint:
+
+    0: Nose
+    1: Left Eye
+    2: Right Eye
+    3: Left Ear
+    4: Right Ear
+    5: Left Shoulder
+    6: Right Shoulder
+    7: Left Elbow
+    8: Right Elbow
+    9: Left Wrist
+    10: Right Wrist
+    11: Left Hip
+    12: Right Hip
+    13: Left Knee
+    14: Right Knee
+    15: Left Ankle
+    16: Right Ankle
+
 ## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
 
 YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.

ultralytics/engine/results.py

@@ -120,7 +120,12 @@ class Results(SimpleClass):
             boxes (torch.tensor, optional): A 2D tensor of bounding box coordinates for each detection.
             masks (torch.tensor, optional): A 3D tensor of detection masks, where each mask is a binary image.
             probs (torch.tensor, optional): A 1D tensor of probabilities of each class for classification task.
-            keypoints (torch.tensor, optional): A 2D tensor of keypoint coordinates for each detection.
+            keypoints (torch.tensor, optional): A 2D tensor of keypoint coordinates for each detection. For default pose
+                model, Keypoint indices for human body pose estimation are:
+                0: Nose, 1: Left Eye, 2: Right Eye, 3: Left Ear, 4: Right Ear
+                5: Left Shoulder, 6: Right Shoulder, 7: Left Elbow, 8: Right Elbow
+                9: Left Wrist, 10: Right Wrist, 11: Left Hip, 12: Right Hip
+                13: Left Knee, 14: Right Knee, 15: Left Ankle, 16: Right Ankle
             obb (torch.tensor, optional): A 2D tensor of oriented bounding box coordinates for each detection.
             speed (dict, optional): A dictionary containing preprocess, inference, and postprocess speeds (ms/image).