Wildlife Recognition Dataset
Wildlife Recognition Dataset
Datasets
Wildlife Recognition Dataset
File
Wildlife Recognition Dataset
Use Case
Wildlife Recognition Dataset
Description
Explore the Wildlife Recognition Dataset with 9,638 high-resolution images across 17 species, perfect for deep learning, wildlife research, and conservation.
Description:
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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Content
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.
Categories
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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