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

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Wang Xin 1 year ago committed by GitHub
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@ -97,7 +97,7 @@ In this example, the `train` directory contains subdirectories for each class in
```bash ```bash
# Start training from a pretrained *.pt model # 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 ## Supported Datasets

@ -12,7 +12,7 @@ keywords: pose estimation, datasets, supported formats, YAML file, object class
** Label Format ** ** 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. 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. 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 # Keypoints
kpt_shape: [num_kpts, dim] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible) 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)] 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. 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: Alternatively, you can directly define class names like this:
``` ```yaml
names: names:
0: person 0: person
1: bicycle 1: bicycle
@ -118,7 +117,7 @@ TODO
### COCO dataset format to YOLO format ### COCO dataset format to YOLO format
``` ```python
from ultralytics.yolo.data.converter import convert_coco from ultralytics.yolo.data.converter import convert_coco
convert_coco(labels_dir='../coco/annotations/', use_keypoints=True) convert_coco(labels_dir='../coco/annotations/', use_keypoints=True)

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