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ultralytics 8.0.141 create new SettingsManager (#3790)

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This commit is contained in:
Glenn Jocher
2023-07-23 16:03:34 +02:00
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parent 42afe772d5
commit 20f5efd40a
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8
docs/datasets/pose/coco.md
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@ -48,10 +48,10 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 epochs with an im
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-pose.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='coco-pose.yaml', epochs=100, imgsz=640)
```
@ -79,7 +79,7 @@ If you use the COCO-Pose dataset in your research or development work, please ci
```bibtex
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
title={Microsoft COCO: Common Objects in Context},
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
year={2015},
eprint={1405.0312},
@ -88,4 +88,4 @@ If you use the COCO-Pose dataset in your research or development work, please ci
}
```
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO-Pose dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO-Pose dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

8
docs/datasets/pose/coco8-pose.md
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@ -37,10 +37,10 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-pose.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
```
@ -68,7 +68,7 @@ If you use the COCO dataset in your research or development work, please cite th
```bibtex
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
title={Microsoft COCO: Common Objects in Context},
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
year={2015},
eprint={1405.0312},
@ -77,4 +77,4 @@ If you use the COCO dataset in your research or development work, please cite th
}
```
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

8
docs/datasets/pose/index.md
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@ -70,10 +70,10 @@ For example if we assume five keypoints of facial landmark: [left eye, right eye
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-pose.pt') # load a pretrained model (recommended for training)
@ -81,7 +81,7 @@ For example if we assume five keypoints of facial landmark: [left eye, right eye
model.train(data='coco128-pose.yaml', epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
yolo detect train data=coco128-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
@ -125,4 +125,4 @@ from ultralytics.data.converter import convert_coco
convert_coco(labels_dir='../coco/annotations/', use_keypoints=True)
```
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.