Update to lowercase MkDocs admonitions (#15990)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
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@ -35,7 +35,7 @@ This dataset can be applied in various computer vision tasks such as object dete
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A YAML (Yet Another Markup Language) file defines the dataset configuration, including paths, classes, and other pertinent details. For the African wildlife dataset, the `african-wildlife.yaml` file is located at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/african-wildlife.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/african-wildlife.yaml).
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!!! Example "ultralytics/cfg/datasets/african-wildlife.yaml"
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!!! example "ultralytics/cfg/datasets/african-wildlife.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/african-wildlife.yaml"
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@ -45,7 +45,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
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To train a YOLOv8n model on the African wildlife dataset for 100 epochs with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -66,7 +66,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 epochs with an
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yolo detect train data=african-wildlife.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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!!! Example "Inference Example"
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!!! example "Inference Example"
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=== "Python"
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@ -111,7 +111,7 @@ The African Wildlife Dataset includes images of four common animal species found
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You can train a YOLOv8 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLOv8n model for 100 epochs with an image size of 640:
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!!! Example
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!!! example
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=== "Python"
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@ -8,7 +8,7 @@ keywords: Argoverse dataset, autonomous driving, 3D tracking, motion forecasting
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The [Argoverse](https://www.argoverse.org/) dataset is a collection of data designed to support research in autonomous driving tasks, such as 3D tracking, motion forecasting, and stereo depth estimation. Developed by Argo AI, the dataset provides a wide range of high-quality sensor data, including high-resolution images, LiDAR point clouds, and map data.
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!!! Note
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!!! note
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The Argoverse dataset `*.zip` file required for training was removed from Amazon S3 after the shutdown of Argo AI by Ford, but we have made it available for manual download on [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link).
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@ -35,7 +35,7 @@ The Argoverse dataset is widely used for training and evaluating deep learning m
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Argoverse dataset, the `Argoverse.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml).
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!!! Example "ultralytics/cfg/datasets/Argoverse.yaml"
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!!! example "ultralytics/cfg/datasets/Argoverse.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/Argoverse.yaml"
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To train a YOLOv8n model on the Argoverse dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -80,7 +80,7 @@ The example showcases the variety and complexity of the data in the Argoverse da
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If you use the Argoverse dataset in your research or development work, please cite the following paper:
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!!! Quote ""
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!!! quote ""
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=== "BibTeX"
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@ -106,7 +106,7 @@ The [Argoverse](https://www.argoverse.org/) dataset, developed by Argo AI, suppo
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To train a YOLOv8 model with the Argoverse dataset, use the provided YAML configuration file and the following code:
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -34,7 +34,7 @@ The application of brain tumor detection using computer vision enables early dia
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the brain tumor dataset, the `brain-tumor.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/brain-tumor.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/brain-tumor.yaml).
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!!! Example "ultralytics/cfg/datasets/brain-tumor.yaml"
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!!! example "ultralytics/cfg/datasets/brain-tumor.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/brain-tumor.yaml"
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To train a YOLOv8n model on the brain tumor dataset for 100 epochs with an image size of 640, utilize the provided code snippets. For a detailed list of available arguments, consult the model's [Training](../../modes/train.md) page.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -65,7 +65,7 @@ To train a YOLOv8n model on the brain tumor dataset for 100 epochs with an image
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yolo detect train data=brain-tumor.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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!!! Example "Inference Example"
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!!! example "Inference Example"
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=== "Python"
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@ -110,7 +110,7 @@ The brain tumor dataset is divided into two subsets: the **training set** consis
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You can train a YOLOv8 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -142,7 +142,7 @@ Using the brain tumor dataset in AI projects enables early diagnosis and treatme
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Inference using a fine-tuned YOLOv8 model can be performed with either Python or CLI approaches. Here are the examples:
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!!! Example "Inference Example"
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!!! example "Inference Example"
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=== "Python"
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@ -52,7 +52,7 @@ The COCO dataset is widely used for training and evaluating deep learning models
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO dataset, the `coco.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
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!!! Example "ultralytics/cfg/datasets/coco.yaml"
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!!! example "ultralytics/cfg/datasets/coco.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/coco.yaml"
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To train a YOLOv8n model on the COCO dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -97,7 +97,7 @@ The example showcases the variety and complexity of the images in the COCO datas
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If you use the COCO dataset in your research or development work, please cite the following paper:
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!!! Quote ""
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!!! quote ""
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=== "BibTeX"
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@ -124,7 +124,7 @@ The [COCO dataset](https://cocodataset.org/#home) (Common Objects in Context) is
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To train a YOLOv8 model using the COCO dataset, you can use the following code snippets:
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -27,7 +27,7 @@ This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8 dataset, the `coco8.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml).
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!!! Example "ultralytics/cfg/datasets/coco8.yaml"
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!!! example "ultralytics/cfg/datasets/coco8.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/coco8.yaml"
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@ -37,7 +37,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
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To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -72,7 +72,7 @@ The example showcases the variety and complexity of the images in the COCO8 data
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If you use the COCO dataset in your research or development work, please cite the following paper:
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!!! Quote ""
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!!! quote ""
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=== "BibTeX"
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@ -99,7 +99,7 @@ The Ultralytics COCO8 dataset is a compact yet versatile object detection datase
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To train a YOLOv8 model using the COCO8 dataset, you can employ either Python or CLI commands. Here's how you can start:
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -30,7 +30,7 @@ The Global Wheat Head Dataset is widely used for training and evaluating deep le
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Global Wheat Head Dataset, the `GlobalWheat2020.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml).
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!!! Example "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
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!!! example "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
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To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -75,7 +75,7 @@ The example showcases the variety and complexity of the data in the Global Wheat
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If you use the Global Wheat Head Dataset in your research or development work, please cite the following paper:
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!!! Quote ""
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!!! quote ""
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=== "BibTeX"
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@ -100,7 +100,7 @@ The Global Wheat Head Dataset is primarily used for developing and training deep
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To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -48,7 +48,7 @@ When using the Ultralytics YOLO format, organize your training and validation im
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Here's how you can use these formats to train your model:
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!!! Example
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!!! example
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=== "Python"
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@ -100,7 +100,7 @@ If you have your own dataset and would like to use it for training detection mod
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You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
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!!! Example
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!!! example
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=== "Python"
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@ -164,7 +164,7 @@ Each dataset page provides detailed information on the structure and usage tailo
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To start training a YOLOv8 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:
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!!! Example
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!!! example
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=== "Python"
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@ -48,7 +48,7 @@ The LVIS dataset is widely used for training and evaluating deep learning models
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the LVIS dataset, the `lvis.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml).
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!!! Example "ultralytics/cfg/datasets/lvis.yaml"
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!!! example "ultralytics/cfg/datasets/lvis.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/lvis.yaml"
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@ -58,7 +58,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
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To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -93,7 +93,7 @@ The example showcases the variety and complexity of the images in the LVIS datas
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If you use the LVIS dataset in your research or development work, please cite the following paper:
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!!! Quote ""
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!!! quote ""
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=== "BibTeX"
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@ -118,7 +118,7 @@ The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale dataset with f
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To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size of 640, follow the example below. This process utilizes Ultralytics' framework, which offers comprehensive training features.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Objects365 Dataset, the `Objects365.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml).
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!!! Example "ultralytics/cfg/datasets/Objects365.yaml"
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!!! example "ultralytics/cfg/datasets/Objects365.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/Objects365.yaml"
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To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -75,7 +75,7 @@ The example showcases the variety and complexity of the data in the Objects365 d
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If you use the Objects365 dataset in your research or development work, please cite the following paper:
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!!! Quote ""
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!!! quote ""
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=== "BibTeX"
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@ -101,7 +101,7 @@ The [Objects365 dataset](https://www.objects365.org/) is designed for object det
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To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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Typically, datasets come with a YAML (Yet Another Markup Language) file that delineates the dataset's configuration. For the case of Open Images V7, a hypothetical `OpenImagesV7.yaml` might exist. For accurate paths and configurations, one should refer to the dataset's official repository or documentation.
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!!! Example "OpenImagesV7.yaml"
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!!! example "OpenImagesV7.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/open-images-v7.yaml"
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@ -71,7 +71,7 @@ Typically, datasets come with a YAML (Yet Another Markup Language) file that del
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To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
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!!! Warning
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!!! warning
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The complete Open Images V7 dataset comprises 1,743,042 training images and 41,620 validation images, requiring approximately **561 GB of storage space** upon download.
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@ -80,7 +80,7 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an im
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- Verify that your device has enough storage capacity.
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- Ensure a robust and speedy internet connection.
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -115,7 +115,7 @@ Researchers can gain invaluable insights into the array of computer vision chall
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For those employing Open Images V7 in their work, it's prudent to cite the relevant papers and acknowledge the creators:
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!!! Quote ""
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!!! quote ""
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=== "BibTeX"
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@ -140,7 +140,7 @@ Open Images V7 is an extensive and versatile dataset created by Google, designed
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To train a YOLOv8 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLOv8n model for 100 epochs with an image size of 640:
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!!! Example "Train Example"
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!!! example "Train Example"
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=== "Python"
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@ -37,11 +37,11 @@ This structure enables a diverse and extensive testing ground for object detecti
|
|||
|
||||
Dataset benchmarking evaluates machine learning model performance on specific datasets using standardized metrics like accuracy, mean average precision and F1-score.
|
||||
|
||||
!!! Tip "Benchmarking"
|
||||
!!! tip "Benchmarking"
|
||||
|
||||
Benchmarking results will be stored in "ultralytics-benchmarks/evaluation.txt"
|
||||
|
||||
!!! Example "Benchmarking example"
|
||||
!!! example "Benchmarking example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -113,7 +113,7 @@ The diversity in the Roboflow 100 benchmark that can be seen above is a signific
|
|||
|
||||
If you use the Roboflow 100 dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! Quote ""
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
|
|
@ -139,7 +139,7 @@ The **Roboflow 100** dataset, developed by [Roboflow](https://roboflow.com/?ref=
|
|||
|
||||
To use the Roboflow 100 dataset for benchmarking, you can implement the RF100Benchmark class from the Ultralytics library. Here's a brief example:
|
||||
|
||||
!!! Example "Benchmarking example"
|
||||
!!! example "Benchmarking example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -203,7 +203,7 @@ The **Roboflow 100** dataset is accessible on [GitHub](https://github.com/robofl
|
|||
|
||||
When using the Roboflow 100 dataset in your research, ensure to properly cite it. Here is the recommended citation:
|
||||
|
||||
!!! Quote ""
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ This dataset can be applied in various computer vision tasks such as object dete
|
|||
|
||||
A YAML (Yet Another Markup Language) file defines the dataset configuration, including paths and classes information. For the signature detection dataset, the `signature.yaml` file is located at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
|
||||
|
||||
!!! Example "ultralytics/cfg/datasets/signature.yaml"
|
||||
!!! example "ultralytics/cfg/datasets/signature.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/signature.yaml"
|
||||
|
|
@ -33,7 +33,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
|
|||
|
||||
To train a YOLOv8n model on the signature detection dataset for 100 epochs with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -54,7 +54,7 @@ To train a YOLOv8n model on the signature detection dataset for 100 epochs with
|
|||
yolo detect train data=signature.yaml model=yolov8n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! Example "Inference Example"
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -102,7 +102,7 @@ To train a YOLOv8n model on the Signature Detection Dataset, follow these steps:
|
|||
1. Download the `signature.yaml` dataset configuration file from [signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
|
||||
2. Use the following Python script or CLI command to start training:
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -140,7 +140,7 @@ To perform inference using a model trained on the Signature Detection Dataset, f
|
|||
1. Load your fine-tuned model.
|
||||
2. Use the below Python script or CLI command to perform inference:
|
||||
|
||||
!!! Example "Inference Example"
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
|
|||
|
|
@ -43,7 +43,7 @@ The SKU-110k dataset is widely used for training and evaluating deep learning mo
|
|||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the SKU-110K dataset, the `SKU-110K.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml).
|
||||
|
||||
!!! Example "ultralytics/cfg/datasets/SKU-110K.yaml"
|
||||
!!! example "ultralytics/cfg/datasets/SKU-110K.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/SKU-110K.yaml"
|
||||
|
|
@ -53,7 +53,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -88,7 +88,7 @@ The example showcases the variety and complexity of the data in the SKU-110k dat
|
|||
|
||||
If you use the SKU-110k dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! Quote ""
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
|
|
@ -113,7 +113,7 @@ The SKU-110k dataset consists of densely packed retail shelf images designed to
|
|||
|
||||
Training a YOLOv8 model on the SKU-110k dataset is straightforward. Here's an example to train a YOLOv8n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -165,7 +165,7 @@ These features make the SKU-110k dataset particularly valuable for training and
|
|||
|
||||
If you use the SKU-110k dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! Quote ""
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ The VisDrone dataset is widely used for training and evaluating deep learning mo
|
|||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the Visdrone dataset, the `VisDrone.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml).
|
||||
|
||||
!!! Example "ultralytics/cfg/datasets/VisDrone.yaml"
|
||||
!!! example "ultralytics/cfg/datasets/VisDrone.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/VisDrone.yaml"
|
||||
|
|
@ -49,7 +49,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
To train a YOLOv8n model on the VisDrone dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -84,7 +84,7 @@ The example showcases the variety and complexity of the data in the VisDrone dat
|
|||
|
||||
If you use the VisDrone dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! Quote ""
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
|
|
@ -117,7 +117,7 @@ The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-
|
|||
|
||||
To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -161,7 +161,7 @@ The configuration file for the VisDrone dataset, `VisDrone.yaml`, can be found i
|
|||
|
||||
If you use the VisDrone dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! Quote ""
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ The VOC dataset is widely used for training and evaluating deep learning models
|
|||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the VOC dataset, the `VOC.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml).
|
||||
|
||||
!!! Example "ultralytics/cfg/datasets/VOC.yaml"
|
||||
!!! example "ultralytics/cfg/datasets/VOC.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/VOC.yaml"
|
||||
|
|
@ -41,7 +41,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -76,7 +76,7 @@ The example showcases the variety and complexity of the images in the VOC datase
|
|||
|
||||
If you use the VOC dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! Quote ""
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
|
|
@ -103,7 +103,7 @@ The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes
|
|||
|
||||
To train a YOLOv8 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLOv8n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@ The xView dataset is widely used for training and evaluating deep learning model
|
|||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the xView dataset, the `xView.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/xView.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/xView.yaml).
|
||||
|
||||
!!! Example "ultralytics/cfg/datasets/xView.yaml"
|
||||
!!! example "ultralytics/cfg/datasets/xView.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/xView.yaml"
|
||||
|
|
@ -44,7 +44,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
|
|||
|
||||
To train a model on the xView dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -79,7 +79,7 @@ The example showcases the variety and complexity of the data in the xView datase
|
|||
|
||||
If you use the xView dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! Quote ""
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
|
|
@ -106,7 +106,7 @@ The [xView](http://xviewdataset.org/) dataset is one of the largest publicly ava
|
|||
|
||||
To train a model on the xView dataset using Ultralytics YOLO, follow these steps:
|
||||
|
||||
!!! Example "Train Example"
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -147,7 +147,7 @@ The xView dataset comprises high-resolution satellite images collected from Worl
|
|||
|
||||
If you utilize the xView dataset in your research, please cite the following paper:
|
||||
|
||||
!!! Quote ""
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue