FeliBreed – A Multi-Breed Cat Image Dataset

FeliBreed – A Multi-Breed Cat Image Dataset We present the FeliBreed dataset, a carefully curated collection of 300 real-world cat images from a variety of breeds. This dataset is designed to support robust image analysis, especially in tasks related to cat breed classification. Each image has been sourced from real-life settings, captured through fieldwork with cameras, ensuring the data is authentic and representative of different environments. Breakdown of the Dataset: • Tabby Cat: 28 images • Mixed Breed: 41 images • Local: 59 images • Bengal Cat: 14 images • Persian: 158 images Key Features: • Real-World Images: All images are sourced from real-life settings, making the dataset highly applicable for model training and validation. • Multi-Breed Representation: The dataset includes multiple breeds, ensuring diversity in the training data and improving the generalization of models. • High-Quality Data: The images are captured using professional cameras during fieldwork, guaranteeing high resolution and accuracy for image classification tasks. FeliBreed is an excellent resource for researchers and developers working on cat breed recognition and image classification tasks. By leveraging this dataset, machine learning models can achieve improved accuracy and robustness when deployed in real-world applications.