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+---
+comments: true
+description: African Wildlife objects detection, a leading dataset for object detection in forests, integrates with Ultralytics. Discover ways to use it for training YOLO models.
+keywords: Ultralytics, African Wildlife dataset, object detection, YOLO, YOLO model training, object tracking, computer vision, deep learning models, forest research, animals tracking
+---
+
+# 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.
+
+## Dataset Structure
+
+The African wildlife objects detection dataset is split into three subsets:
+
+- **Training set**: Contains 1052 images, each with corresponding annotations.
+- **Validation set**: Includes 225 images, each with paired annotations.
+- **Testing set**: Comprises 227 images, each with paired annotations.
+
+## 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.
+
+## Dataset YAML
+
+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).
+
+!!! Example "ultralytics/cfg/datasets/african-wildlife.yaml"
+
+ ```yaml
+ --8<-- "ultralytics/cfg/datasets/african-wildlife.yaml"
+ ```
+
+## 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.
+
+!!! Example "Train Example"
+
+ === "Python"
+
+ ```python
+ from ultralytics import YOLO
+
+ # Load a model
+ model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
+
+ # Train the model
+ results = model.train(data='african-wildlife.yaml', epochs=100, imgsz=640)
+ ```
+
+ === "CLI"
+
+ ```bash
+ # Start training from a pretrained *.pt model
+ yolo detect train data=african-wildlife.yaml model=yolov8n.pt epochs=100 imgsz=640
+ ```
+
+!!! Example "Inference Example"
+
+ === "Python"
+
+ ```python
+ from ultralytics import YOLO
+
+ # Load a model
+ model = YOLO('path/to/best.pt') # load a brain-tumor fine-tuned model
+
+ # Inference using the model
+ results = model.predict("https://ultralytics.com/assets/african-wildlife-sample.jpg")
+ ```
+
+ === "CLI"
+
+ ```bash
+ # Start prediction with a finetuned *.pt model
+ yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/african-wildlife-sample.jpg"
+ ```
+
+
+## Sample Images and Annotations
+
+The African wildlife dataset comprises a wide variety of images showcasing diverse animal species and their natural habitats. Below are examples of images from the dataset, each accompanied by its corresponding annotations.
+
+
+
+- **Mosaiced Image**: Here, we present a training batch consisting of mosaiced dataset images. Mosaicing, a training technique, combines multiple images into one, enriching batch diversity. This method helps enhance the model's ability to generalize across different object sizes, aspect ratios, and contexts.
+
+This example illustrates the variety and complexity of images in the African wildlife dataset, emphasizing the benefits of including mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
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diff --git a/docs/en/datasets/detect/index.md b/docs/en/datasets/detect/index.md
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@@ -82,6 +82,8 @@ Here is a list of the supported datasets and a brief description for each:
- [**VisDrone**](visdrone.md): A dataset focusing on drone-based images, containing various object categories like cars, pedestrians, and cyclists.
- [**VOC**](voc.md): PASCAL VOC is a popular object detection dataset with 20 object categories including vehicles, animals, and furniture.
- [**xView**](xview.md): A dataset containing high-resolution satellite imagery, designed for the detection of various object classes in overhead views.
+- [**Brain-tumor**](brain-tumor.md): This dataset comprises MRI or CT scan images containing information about brain tumor presence, location, and characteristics. It plays a crucial role in training computer vision models to automate tumor identification, facilitating early diagnosis and treatment planning.
+- [**African-wildlife**](african-wildlife.md): Featuring images of African wildlife such as buffalo, elephant, rhino, and zebra, this dataset is instrumental in training computer vision models. It is indispensable for identifying animals across different habitats and contributes significantly to wildlife research endeavors.
### Adding your own dataset
diff --git a/docs/en/datasets/index.md b/docs/en/datasets/index.md
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- [VOC](detect/voc.md): The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
- [xView](detect/xview.md): A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
- [Roboflow 100](detect/roboflow-100.md): A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
+- [Brain-tumor](detect/brain-tumor.md): A dataset for detecting brain tumors includes MRI or CT scan images with details on tumor presence, location, and characteristics. It's vital for training computer vision models to automate tumor identification, aiding in early diagnosis and treatment planning.
+- [African-wildlife](detect/african-wildlife.md): A dataset featuring images of African wildlife, including buffalo, elephant, rhino, and zebra, aids in training computer vision models. Essential for identifying animals in various habitats, it supports wildlife research.
## [Instance Segmentation Datasets](segment/index.md)
diff --git a/docs/en/tasks/pose.md b/docs/en/tasks/pose.md
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@@ -12,16 +12,28 @@ Pose estimation is a task that involves identifying the location of specific poi
The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually along with the confidence scores for each point. Pose estimation is a good choice when you need to identify specific parts of an object in a scene, and their location in relation to each other.
-