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:
- Dogs
- Cats
- Birds
- Fish
- Horses
- Elephants
- Lions
- Tigers
- Bears
- Rabbits
Model Architecture
The classification model uses a convolutional neural network (CNN) architecture optimized for image recognition:
- Input Layer: Accepts RGB images of varying sizes
- Convolutional Layers: Multiple conv layers with ReLU activation
- Pooling Layers: Max pooling for dimensionality reduction
- Dropout Layers: Regularization to prevent overfitting
- Dense Layers: Fully connected layers for final classification
- Output Layer: Softmax activation for 10-class probability distribution
Data Preprocessing
The dataset undergoes several preprocessing steps:
- Image resizing to standard dimensions
- Normalization of pixel values
- Data augmentation (rotation, flipping, scaling)
- Train/validation/test split
Performance Metrics
The model achieves excellent performance across all metrics:
- Accuracy: 92.5% on test set
- Precision: High precision across all animal categories
- Recall: Consistent recall rates for balanced classification
- F1-Score: Strong F1 scores indicating robust performance
Key Features
- Real-time image classification
- Confidence score prediction
- Visualization of model predictions
- Confusion matrix analysis
- Feature map visualization
Technologies Used
- Python
- TensorFlow/Keras
- OpenCV
- NumPy
- Matplotlib
- Scikit-learn
- Jupyter Notebook
Applications
This classifier can be applied to various real-world scenarios:
- Wildlife monitoring and conservation
- Educational tools for animal identification
- Pet identification systems
- Automated photo tagging