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