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ultralytics 8.0.141 create new SettingsManager (#3790)

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6
docs/datasets/classify/caltech101.md
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@ -34,10 +34,10 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='caltech101', epochs=100, imgsz=416)
```
@ -74,4 +74,4 @@ If you use the Caltech-101 dataset in your research or development work, please
}
```
We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creating and maintaining the Caltech-101 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the Caltech-101 dataset and its creators, visit the [Caltech-101 dataset website](https://data.caltech.edu/records/mzrjq-6wc02).
We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creating and maintaining the Caltech-101 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the Caltech-101 dataset and its creators, visit the [Caltech-101 dataset website](https://data.caltech.edu/records/mzrjq-6wc02).

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docs/datasets/classify/caltech256.md
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@ -34,10 +34,10 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='caltech256', epochs=100, imgsz=416)
```
@ -71,4 +71,4 @@ If you use the Caltech-256 dataset in your research or development work, please
We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the
Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).
Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).

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docs/datasets/classify/cifar10.md
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@ -37,10 +37,10 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='cifar10', epochs=100, imgsz=32)
```
@ -73,4 +73,4 @@ If you use the CIFAR-10 dataset in your research or development work, please cit
}
```
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).

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docs/datasets/classify/cifar100.md
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@ -37,10 +37,10 @@ To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='cifar100', epochs=100, imgsz=32)
```
@ -73,4 +73,4 @@ If you use the CIFAR-100 dataset in your research or development work, please ci
}
```
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-100 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-100 dataset and its creator, visit the [CIFAR-100 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-100 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-100 dataset and its creator, visit the [CIFAR-100 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).

6
docs/datasets/classify/fashion-mnist.md
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@ -51,10 +51,10 @@ To train a CNN model on the Fashion-MNIST dataset for 100 epochs with an image s
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='fashion-mnist', epochs=100, imgsz=28)
```
@ -76,4 +76,4 @@ The example showcases the variety and complexity of the images in the Fashion-MN
## Acknowledgments
If you use the Fashion-MNIST dataset in your research or development work, please acknowledge the dataset by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This dataset was made available by Zalando Research.
If you use the Fashion-MNIST dataset in your research or development work, please acknowledge the dataset by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This dataset was made available by Zalando Research.

6
docs/datasets/classify/imagenet.md
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@ -37,10 +37,10 @@ To train a deep learning model on the ImageNet dataset for 100 epochs with an im
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='imagenet', epochs=100, imgsz=224)
```
@ -76,4 +76,4 @@ If you use the ImageNet dataset in your research or development work, please cit
}
```
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).

6
docs/datasets/classify/imagenet10.md
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@ -33,10 +33,10 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='imagenet10', epochs=5, imgsz=224)
```
@ -71,4 +71,4 @@ If you use the ImageNet10 dataset in your research or development work, please c
}
```
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset. The ImageNet10 dataset, while a compact subset, is a valuable resource for quick testing and debugging in the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset. The ImageNet10 dataset, while a compact subset, is a valuable resource for quick testing and debugging in the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).

14
docs/datasets/classify/imagenette.md
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@ -35,10 +35,10 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='imagenette', epochs=100, imgsz=224)
```
@ -70,10 +70,10 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model with ImageNette160
model.train(data='imagenette160', epochs=100, imgsz=160)
```
@ -91,10 +91,10 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model with ImageNette320
model.train(data='imagenette320', epochs=100, imgsz=320)
```
@ -110,4 +110,4 @@ These smaller versions of the dataset allow for rapid iterations during the deve
## Citations and Acknowledgments
If you use the ImageNette dataset in your research or development work, please acknowledge it appropriately. For more information about the ImageNette dataset, visit the [ImageNette dataset GitHub page](https://github.com/fastai/imagenette).
If you use the ImageNette dataset in your research or development work, please acknowledge it appropriately. For more information about the ImageNette dataset, visit the [ImageNette dataset GitHub page](https://github.com/fastai/imagenette).

6
docs/datasets/classify/imagewoof.md
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@ -32,10 +32,10 @@ To train a CNN model on the ImageWoof dataset for 100 epochs with an image size
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='imagewoof', epochs=100, imgsz=224)
```
@ -81,4 +81,4 @@ The example showcases the subtle differences and similarities among the differen
If you use the ImageWoof dataset in your research or development work, please make sure to acknowledge the creators of the dataset by linking to the [official dataset repository](https://github.com/fastai/imagenette). As of my knowledge cutoff in September 2021, there is no official publication specifically about ImageWoof for citation.
We would like to acknowledge the FastAI team for creating and maintaining the ImageWoof dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageWoof dataset, visit the [ImageWoof dataset repository](https://github.com/fastai/imagenette).
We would like to acknowledge the FastAI team for creating and maintaining the ImageWoof dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageWoof dataset, visit the [ImageWoof dataset repository](https://github.com/fastai/imagenette).

8
docs/datasets/classify/index.md
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@ -83,10 +83,10 @@ In this example, the `train` directory contains subdirectories for each class in
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
@ -94,7 +94,7 @@ In this example, the `train` directory contains subdirectories for each class in
model.train(data='path/to/dataset', epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640
@ -117,4 +117,4 @@ Ultralytics supports the following datasets with automatic download:
### Adding your own dataset
If you have your own dataset and would like to use it for training classification models with Ultralytics, ensure that it follows the format specified above under "Dataset format" and then point your `data` argument to the dataset directory.
If you have your own dataset and would like to use it for training classification models with Ultralytics, ensure that it follows the format specified above under "Dataset format" and then point your `data` argument to the dataset directory.

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docs/datasets/classify/mnist.md
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@ -40,10 +40,10 @@ To train a CNN model on the MNIST dataset for 100 epochs with an image size of 3
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='mnist', epochs=100, imgsz=32)
```
@ -79,4 +79,4 @@ research or development work, please cite the following paper:
}
```
We would like to acknowledge Yann LeCun, Corinna Cortes, and Christopher J.C. Burges for creating and maintaining the MNIST dataset as a valuable resource for the machine learning and computer vision research community. For more information about the MNIST dataset and its creators, visit the [MNIST dataset website](http://yann.lecun.com/exdb/mnist/).
We would like to acknowledge Yann LeCun, Corinna Cortes, and Christopher J.C. Burges for creating and maintaining the MNIST dataset as a valuable resource for the machine learning and computer vision research community. For more information about the MNIST dataset and its creators, visit the [MNIST dataset website](http://yann.lecun.com/exdb/mnist/).