Update datasets/classify/index.md Docs (#3244)

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

docs/datasets/classify/index.md

@@ -97,9 +97,9 @@ In this example, the `train` directory contains subdirectories for each class in
     
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=path/to/data model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640
         ```
 
 ## Supported Datasets
 
-TODO
+TODO

docs/datasets/pose/index.md

@@ -12,7 +12,7 @@ keywords: pose estimation, datasets, supported formats, YAML file, object class
 
 ** Label Format **
 
-The dataset format used for training YOLO segmentation models is as follows:
+The dataset format used for training YOLO pose 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.
@@ -52,7 +52,6 @@ names: [<class-1>, <class-2>, ..., <class-n>]
 # Keypoints
 kpt_shape: [num_kpts, dim]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
 flip_idx: [n1, n2 ... , n(num_kpts)]
-
 ```
 
 The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
@@ -65,7 +64,7 @@ NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory
 
 Alternatively, you can directly define class names like this:
 
-```
+```yaml
 names:
   0: person
   1: bicycle
@@ -118,8 +117,8 @@ TODO
 
 ### COCO dataset format to YOLO format
 
-```
+```python
 from ultralytics.yolo.data.converter import convert_coco
 
 convert_coco(labels_dir='../coco/annotations/', use_keypoints=True)
-```
+```