docs/en/datasets/detect/african-wildlife.md
The Ultralytics African Wildlife Dataset is an object detection dataset of 1,504 images across 4 animal classes — buffalo, elephant, rhino, and zebra — commonly found in South African nature reserves. The images are pre-split into 1,052 training, 225 validation, and 227 test images, and the dataset downloads automatically (~100 MB) the first time you train. It is a compact, ready-to-use benchmark for training computer vision models for wildlife monitoring, conservation, and ecological research.
<p align="center"> <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/EXYB-dbgJjY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen> </iframe><strong>Watch:</strong> How to Train Ultralytics YOLO26 on the African Wildlife Dataset | Inference, Metrics & ONNX Export 🐘
</p>The Ultralytics African Wildlife Dataset contains 1,504 images across 4 classes (buffalo, elephant, rhino, and zebra), pre-split into three subsets:
!!! tip "Automatic download"
The African Wildlife Dataset (~100 MB) downloads automatically the first time you start training, so no manual download or preparation is required.
Explore African Wildlife on Ultralytics Platform to browse the images with their annotation overlays, view the class distribution and bounding-box heatmaps in the Charts tab, and clone it to train your own model in the cloud.
The Ultralytics African Wildlife Dataset supports a range of object detection applications:
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.
!!! example "ultralytics/cfg/datasets/african-wildlife.yaml"
```yaml
--8<-- "ultralytics/cfg/datasets/african-wildlife.yaml"
```
To train a YOLO26n 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 page.
!!! example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n.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=yolo26n.pt epochs=100 imgsz=640
```
Once trained, run inference with the fine-tuned model on new images:
!!! example "Inference Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("path/to/best.pt") # load an African wildlife 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"
```
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.
We'd like to thank the original dataset author, Bianca Ferreira, for releasing this dataset to the community. The Ultralytics team has updated and adapted it internally so it can be used seamlessly with Ultralytics YOLO models. This dataset is available under the AGPL-3.0 License.
If you use this dataset in your research, please cite it using the mentioned details:
!!! quote ""
=== "BibTeX"
```bibtex
@dataset{Ferreira_African_Wildlife_Ultralytics_Adaptation_2024,
author = {Ferreira, Bianca},
title = {African Wildlife Detection Dataset (Ultralytics YOLO Adaptation)},
url = {https://docs.ultralytics.com/datasets/detect/african-wildlife/},
note = {Original dataset by Bianca Ferreira; adapted for Ultralytics YOLO by Glenn Jocher and Muhammad Rizwan Munawar},
license = {AGPL-3.0},
version = {1.0.0},
year = {2024}
}
```
The African Wildlife Dataset is an object detection dataset of 1,504 images across 4 animal classes — buffalo, elephant, rhino, and zebra — found in South African nature reserves. It is used to train and evaluate models for identifying African wildlife in images, which supports wildlife conservation, ecological research, and monitoring in natural reserves. It also serves as an accessible resource for students and researchers studying computer vision.
The Ultralytics African Wildlife Dataset contains 1,504 images across 4 classes: buffalo, elephant, rhino, and zebra. The images are pre-split into 1,052 training, 225 validation, and 227 test images, and the dataset downloads automatically (~100 MB) the first time you train.
You can train a YOLO26 model on the African Wildlife Dataset by using the african-wildlife.yaml configuration file. Below is an example of how to train the YOLO26n model for 100 epochs with an image size of 640:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n.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=yolo26n.pt epochs=100 imgsz=640
```
For additional training parameters and options, refer to the Training documentation.
The YAML configuration file for the African Wildlife Dataset, named african-wildlife.yaml, can be found at this GitHub link. This file defines the dataset configuration, including paths, classes, and other details crucial for training machine learning models.
The African Wildlife Dataset is available under the AGPL-3.0 License. It was originally released on Kaggle by Bianca Ferreira and adapted by Ultralytics for seamless use with Ultralytics YOLO models. If you use the dataset in your research, please cite it using the BibTeX entry in the Citations section.