ultralytics 8.0.141 create new SettingsManager (#3790)
This commit is contained in:
Glenn Jocher
GitHub
@ -34,10 +34,10 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='caltech101', epochs=100, imgsz=416)
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```
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@ -74,4 +74,4 @@ If you use the Caltech-101 dataset in your research or development work, please
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}
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```
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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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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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@ -34,10 +34,10 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='caltech256', epochs=100, imgsz=416)
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```
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@ -71,4 +71,4 @@ If you use the Caltech-256 dataset in your research or development work, please
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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
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Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).
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Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).
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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
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='cifar10', epochs=100, imgsz=32)
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```
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@ -73,4 +73,4 @@ If you use the CIFAR-10 dataset in your research or development work, please cit
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}
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```
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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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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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@ -37,10 +37,10 @@ To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='cifar100', epochs=100, imgsz=32)
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```
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@ -73,4 +73,4 @@ If you use the CIFAR-100 dataset in your research or development work, please ci
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}
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```
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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).
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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).
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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
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='fashion-mnist', epochs=100, imgsz=28)
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```
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@ -76,4 +76,4 @@ The example showcases the variety and complexity of the images in the Fashion-MN
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## Acknowledgments
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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.
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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.
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@ -37,10 +37,10 @@ To train a deep learning model on the ImageNet dataset for 100 epochs with an im
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='imagenet', epochs=100, imgsz=224)
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```
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@ -76,4 +76,4 @@ If you use the ImageNet dataset in your research or development work, please cit
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}
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```
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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/).
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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/).
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@ -33,10 +33,10 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='imagenet10', epochs=5, imgsz=224)
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```
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@ -71,4 +71,4 @@ If you use the ImageNet10 dataset in your research or development work, please c
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}
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```
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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/).
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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/).
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@ -35,10 +35,10 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='imagenette', epochs=100, imgsz=224)
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```
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@ -70,10 +70,10 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model with ImageNette160
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model.train(data='imagenette160', epochs=100, imgsz=160)
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```
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@ -91,10 +91,10 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model with ImageNette320
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model.train(data='imagenette320', epochs=100, imgsz=320)
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```
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@ -110,4 +110,4 @@ These smaller versions of the dataset allow for rapid iterations during the deve
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## Citations and Acknowledgments
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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).
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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).
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@ -32,10 +32,10 @@ To train a CNN model on the ImageWoof dataset for 100 epochs with an image size
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='imagewoof', epochs=100, imgsz=224)
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```
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@ -81,4 +81,4 @@ The example showcases the subtle differences and similarities among the differen
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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.
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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).
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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).
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@ -83,10 +83,10 @@ In this example, the `train` directory contains subdirectories for each class in
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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@ -94,7 +94,7 @@ In this example, the `train` directory contains subdirectories for each class in
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model.train(data='path/to/dataset', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640
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@ -117,4 +117,4 @@ Ultralytics supports the following datasets with automatic download:
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### Adding your own dataset
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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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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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@ -40,10 +40,10 @@ To train a CNN model on the MNIST dataset for 100 epochs with an image size of 3
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='mnist', epochs=100, imgsz=32)
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```
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@ -79,4 +79,4 @@ research or development work, please cite the following paper:
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}
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```
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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/).
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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/).
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@ -47,10 +47,10 @@ To train a YOLOv8n model on the Argoverse dataset for 100 epochs with an image s
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='Argoverse.yaml', epochs=100, imgsz=640)
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```
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@ -86,4 +86,4 @@ If you use the Argoverse dataset in your research or development work, please ci
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}
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```
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We would like to acknowledge Argo AI for creating and maintaining the Argoverse dataset as a valuable resource for the autonomous driving research community. For more information about the Argoverse dataset and its creators, visit the [Argoverse dataset website](https://www.argoverse.org/).
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We would like to acknowledge Argo AI for creating and maintaining the Argoverse dataset as a valuable resource for the autonomous driving research community. For more information about the Argoverse dataset and its creators, visit the [Argoverse dataset website](https://www.argoverse.org/).
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@ -47,10 +47,10 @@ To train a YOLOv8n model on the COCO dataset for 100 epochs with an image size o
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='coco.yaml', epochs=100, imgsz=640)
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```
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@ -78,7 +78,7 @@ If you use the COCO dataset in your research or development work, please cite th
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```bibtex
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@misc{lin2015microsoft,
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title={Microsoft COCO: Common Objects in Context},
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title={Microsoft COCO: Common Objects in Context},
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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},
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year={2015},
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eprint={1405.0312},
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@ -87,4 +87,4 @@ If you use the COCO dataset in your research or development work, please cite th
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}
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```
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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).
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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).
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@ -37,10 +37,10 @@ To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='coco8.yaml', epochs=100, imgsz=640)
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```
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@ -68,7 +68,7 @@ If you use the COCO dataset in your research or development work, please cite th
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```bibtex
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@misc{lin2015microsoft,
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title={Microsoft COCO: Common Objects in Context},
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title={Microsoft COCO: Common Objects in Context},
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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},
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year={2015},
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eprint={1405.0312},
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@ -77,4 +77,4 @@ If you use the COCO dataset in your research or development work, please cite th
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}
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```
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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).
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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).
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@ -46,10 +46,10 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='GlobalWheat2020.yaml', epochs=100, imgsz=640)
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```
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@ -84,4 +84,4 @@ If you use the Global Wheat Head Dataset in your research or development work, p
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}
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```
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We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](http://www.global-wheat.com/).
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We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](http://www.global-wheat.com/).
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@ -51,10 +51,10 @@ Here's how you can use these formats to train your model:
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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@ -62,7 +62,7 @@ Here's how you can use these formats to train your model:
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model.train(data='coco128.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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@ -100,4 +100,4 @@ convert_coco(labels_dir='../coco/annotations/')
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This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.
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Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.
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Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.
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@ -46,10 +46,10 @@ To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image
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```python
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from ultralytics import YOLO
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# Load a model
|
||||
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
||||
|
||||
|
||||
# Train the model
|
||||
model.train(data='Objects365.yaml', epochs=100, imgsz=640)
|
||||
```
|
||||
@ -85,4 +85,4 @@ If you use the Objects365 dataset in your research or development work, please c
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).
|
||||
We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).
|
||||
|
||||
@ -48,10 +48,10 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image si
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a model
|
||||
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
||||
|
||||
|
||||
# Train the model
|
||||
model.train(data='SKU-110K.yaml', epochs=100, imgsz=640)
|
||||
```
|
||||
@ -86,4 +86,4 @@ If you use the SKU-110k dataset in your research or development work, please cit
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge Eran Goldman et al. for creating and maintaining the SKU-110k dataset as a valuable resource for the computer vision research community. For more information about the SKU-110k dataset and its creators, visit the [SKU-110k dataset GitHub repository](https://github.com/eg4000/SKU110K_CVPR19).
|
||||
We would like to acknowledge Eran Goldman et al. for creating and maintaining the SKU-110k dataset as a valuable resource for the computer vision research community. For more information about the SKU-110k dataset and its creators, visit the [SKU-110k dataset GitHub repository](https://github.com/eg4000/SKU110K_CVPR19).
|
||||
|
||||
@ -44,10 +44,10 @@ To train a YOLOv8n model on the VisDrone dataset for 100 epochs with an image si
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a model
|
||||
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
||||
|
||||
|
||||
# Train the model
|
||||
model.train(data='VisDrone.yaml', epochs=100, imgsz=640)
|
||||
```
|
||||
@ -76,8 +76,8 @@ If you use the VisDrone dataset in your research or development work, please cit
|
||||
```bibtex
|
||||
@ARTICLE{9573394,
|
||||
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
|
||||
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
|
||||
title={Detection and Tracking Meet Drones Challenge},
|
||||
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
|
||||
title={Detection and Tracking Meet Drones Challenge},
|
||||
year={2021},
|
||||
volume={},
|
||||
number={},
|
||||
@ -85,4 +85,4 @@ If you use the VisDrone dataset in your research or development work, please cit
|
||||
doi={10.1109/TPAMI.2021.3119563}}
|
||||
```
|
||||
|
||||
We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset).
|
||||
We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset).
|
||||
|
||||
@ -47,10 +47,10 @@ To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a model
|
||||
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
||||
|
||||
|
||||
# Train the model
|
||||
model.train(data='VOC.yaml', epochs=100, imgsz=640)
|
||||
```
|
||||
@ -79,7 +79,7 @@ If you use the VOC dataset in your research or development work, please cite the
|
||||
|
||||
```bibtex
|
||||
@misc{everingham2010pascal,
|
||||
title={The PASCAL Visual Object Classes (VOC) Challenge},
|
||||
title={The PASCAL Visual Object Classes (VOC) Challenge},
|
||||
author={Mark Everingham and Luc Van Gool and Christopher K. I. Williams and John Winn and Andrew Zisserman},
|
||||
year={2010},
|
||||
eprint={0909.5206},
|
||||
@ -88,4 +88,4 @@ If you use the VOC dataset in your research or development work, please cite the
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the PASCAL VOC Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the VOC dataset and its creators, visit the [PASCAL VOC dataset website](http://host.robots.ox.ac.uk/pascal/VOC/).
|
||||
We would like to acknowledge the PASCAL VOC Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the VOC dataset and its creators, visit the [PASCAL VOC dataset website](http://host.robots.ox.ac.uk/pascal/VOC/).
|
||||
|
||||
@ -50,10 +50,10 @@ To train a model on the xView dataset for 100 epochs with an image size of 640,
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a model
|
||||
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
||||
|
||||
|
||||
# Train the model
|
||||
model.train(data='xView.yaml', epochs=100, imgsz=640)
|
||||
```
|
||||
@ -81,7 +81,7 @@ If you use the xView dataset in your research or development work, please cite t
|
||||
|
||||
```bibtex
|
||||
@misc{lam2018xview,
|
||||
title={xView: Objects in Context in Overhead Imagery},
|
||||
title={xView: Objects in Context in Overhead Imagery},
|
||||
author={Darius Lam and Richard Kuzma and Kevin McGee and Samuel Dooley and Michael Laielli and Matthew Klaric and Yaroslav Bulatov and Brendan McCord},
|
||||
year={2018},
|
||||
eprint={1802.07856},
|
||||
@ -90,4 +90,4 @@ If you use the xView dataset in your research or development work, please cite t
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).
|
||||
We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).
|
||||
|
||||
@ -56,4 +56,4 @@ Image classification is a computer vision task that involves categorizing an ima
|
||||
Multi-object tracking is a computer vision technique that involves detecting and tracking multiple objects over time in a video sequence.
|
||||
|
||||
* [Argoverse](detect/argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations for multi-object tracking tasks.
|
||||
* [VisDrone](detect/visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
|
||||
* [VisDrone](detect/visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
|
||||
|
||||
@ -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).
|
||||
|
||||
@ -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).
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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).
|
||||
|
||||
@ -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).
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -16,15 +16,15 @@ Support for training trackers alone is coming soon
|
||||
!!! example ""
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO('yolov8n.pt')
|
||||
results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True)
|
||||
results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True)
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
|
||||
```bash
|
||||
yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show
|
||||
```
|
||||
```
|
||||
|
||||
Reference in New Issue
Block a user