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docs/en/datasets/detect/african-wildlife.md
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@ -6,7 +6,7 @@ keywords: African Wildlife Dataset, South African animals, object detection, com
# African Wildlife Dataset
This dataset showcases four common animal classes typically found in South African nature reserves. It includes images of African wildlife such as buffalo, elephant, rhino, and zebra, providing valuable insights into their characteristics. Essential for training computer vision algorithms, this dataset aids in identifying animals in various habitats, from zoos to forests, and supports wildlife research.
This dataset showcases four common animal classes typically found in South African nature reserves. It includes images of African wildlife such as buffalo, elephant, rhino, and zebra, providing valuable insights into their characteristics. Essential for training [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) algorithms, this dataset aids in identifying animals in various habitats, from zoos to forests, and supports wildlife research.
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@ -29,7 +29,7 @@ The African wildlife objects detection dataset is split into three subsets:
## Applications
This dataset can be applied in various computer vision tasks such as object detection, object tracking, and research. Specifically, it can be used to train and evaluate models for identifying African wildlife objects in images, which can have applications in wildlife conservation, ecological research, and monitoring efforts in natural reserves and protected areas. Additionally, it can serve as a valuable resource for educational purposes, enabling students and researchers to study and understand the characteristics and behaviors of different animal species.
This dataset can be applied in various computer vision tasks such as [object detection](https://www.ultralytics.com/glossary/object-detection), object tracking, and research. Specifically, it can be used to train and evaluate models for identifying African wildlife objects in images, which can have applications in wildlife conservation, ecological research, and monitoring efforts in natural reserves and protected areas. Additionally, it can serve as a valuable resource for educational purposes, enabling students and researchers to study and understand the characteristics and behaviors of different animal species.
## Dataset YAML
@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
## Usage
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.
To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) 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"
@ -136,7 +136,7 @@ For additional training parameters and options, refer to the [Training](../../mo
### Where can I find the YAML configuration file for the African Wildlife Dataset?
The YAML configuration file for the African Wildlife Dataset, named `african-wildlife.yaml`, can be found at [this GitHub link](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/african-wildlife.yaml). This file defines the dataset configuration, including paths, classes, and other details crucial for training machine learning models. See the [Dataset YAML](#dataset-yaml) section for more details.
The YAML configuration file for the African Wildlife Dataset, named `african-wildlife.yaml`, can be found at [this GitHub link](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/african-wildlife.yaml). This file defines the dataset configuration, including paths, classes, and other details crucial for training [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models. See the [Dataset YAML](#dataset-yaml) section for more details.
### Can I see sample images and annotations from the African Wildlife Dataset?