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ultralytics 8.0.141 create new SettingsManager (#3790)

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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/segment/coco.md
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@ -47,10 +47,10 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='coco-seg.yaml', epochs=100, imgsz=640)
```
@ -78,7 +78,7 @@ If you use the COCO-Seg dataset in your research or development work, please cit
```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},
@ -87,4 +87,4 @@ If you use the COCO-Seg dataset in your research or development work, please cit
}
```
We extend our thanks to the COCO Consortium for creating and maintaining this invaluable 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 extend our thanks to the COCO Consortium for creating and maintaining this invaluable 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/segment/coco8-seg.md
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@ -37,10 +37,10 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an ima
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='coco8-seg.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/segment/index.md
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@ -71,10 +71,10 @@ The `train` and `val` fields specify the paths to the directories containing the
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
@ -82,7 +82,7 @@ The `train` and `val` fields specify the paths to the directories containing the
model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
yolo detect train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
@ -137,4 +137,4 @@ auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt
The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](https://docs.ultralytics.com/models/sam), the device to run the models on, and the output directory for saving the annotated results.
By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.