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Ten Animals Classifier

This project develops a deep learning model for classifying images of ten different animal species using convolutional neural networks. The classifier demonstrates high accuracy in distinguishing between various animal categories.

Project Overview

The goal of this project is to build a robust image classification system that can accurately identify ten different animal species from photographs. The model uses state-of-the-art computer vision techniques to achieve high classification accuracy.

Animal Categories

The classifier is trained to recognize the following ten animal categories:

Model Architecture

The classification model uses a convolutional neural network (CNN) architecture optimized for image recognition:

Data Preprocessing

The dataset undergoes several preprocessing steps:

Performance Metrics

The model achieves excellent performance across all metrics:

Key Features

Technologies Used

Applications

This classifier can be applied to various real-world scenarios: