This project focuses on the development of a Convolutional Neural Network (CNN) to identify different animal species from images. It serves as an initial exploration into the architecture and application of CNNs in the field of species classification.
The goal of this project is to build a CNN capable of distinguishing between eight specific animal species. This foundational work lays the groundwork for future expansion, allowing the model to be scaled to identify a broader range of species.
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Custom CNN Architecture: Developed a CNN tailored for the task of species identification. Various architectures and hyperparameters were tested to optimize model performance.
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Data Preprocessing: Implemented robust data preprocessing techniques including image augmentation, normalization, and resizing to ensure consistent and high-quality input data.
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Model Training and Evaluation: Trained the CNN using a well-curated dataset and evaluated its performance with metrics such as accuracy, precision, recall, and F1-score. Techniques like regularization and dropout were used to prevent overfitting.
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Scalability: The project is designed with scalability in mind, allowing for the model to be easily extended to recognize additional species in the future.