Wildlife Recognition Dataset

Wildlife Recognition Dataset


Wildlife Recognition Dataset


Wildlife Recognition Dataset

Use Case

Wildlife Recognition Dataset


Explore the Wildlife Recognition Dataset with 9,638 high-resolution images across 17 species, perfect for deep learning, wildlife research, and conservation.

Wildlife Recognition Dataset


Welcome to the Wildlife Recognition Dataset, an extensive collection of high-quality images featuring a diverse range of wildlife animals. This dataset is meticulously curated to support deep learning projects focused on wildlife identification and classification. Whether you are a researcher, a machine learning enthusiast, or a wildlife conservationist, this dataset provides a robust foundation for your projects.

About the Dataset

This dataset contains images of wildlife animals captured in their natural habitats, ensuring authenticity and variety. The images are organized into 17 folders, each dedicated to a specific species, making it easy to train and test deep learning models for wildlife recognition.

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17 Organized Folders: Each folder represents a distinct wildlife species, facilitating targeted research and model training.

Rich Diversity: Includes a variety of wildlife such as mammals, birds, reptiles, and more, captured in high resolution.

High-Resolution Images: Images are captured in natural habitats to provide realistic and varied training data.


The dataset contains a total of 9,638 images distributed across 17 classes as follows:

Horse: 743 images

Bald Eagle: 732 images

Black Bear: 706 images

Bobcat: 679 images

Cheetah: 342 images

Cougar: 659 images

Deer: 670 images

Elk: 585 images

Gray Fox: 608 images

Hyena: 303 images

Lion: 289 images

Raccoon: 685 images

Red Fox: 697 images

Rhino: 376 images

Tiger: 265 images

Wolf: 927 images

Zebra: 372 images

Intended Use

This dataset is designed for:

Deep Learning Enthusiasts: Ideal for those looking to build models for wildlife identification and classification.

Researchers: Provides a solid base for research in image recognition, object detection, and species classification.

Conservationists: Helps in developing tools for wildlife monitoring and conservation efforts.

Additional Content

Annotation Files: Each image comes with detailed annotations, including species name, location of capture, and timestamp, to support advanced research and model training.

Preprocessing Scripts: Includes scripts for data preprocessing, augmentation, and normalization to streamline your workflow.

Benchmark Models: Provides pre-trained models and benchmark results to help you get started quickly and compare your model performance.

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Quality Data Creation


Guaranteed TAT


ISO 9001:2015, ISO/IEC 27001:2013 Certified


HIPAA Compliance


GDPR Compliance


Compliance and Security

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