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🧾 Animal Classification – Image-Based Deep Learning Model

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Identifying animal species from images using Convolutional Neural Networks (CNNs), data augmentation, and transfer learning for robust classification.

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📌 Table of Contents

• Overview
• Business Problem
• Dataset
• Tools & Technologies
• Project Structure
• Data Cleaning & Preparation
• Exploratory Data Analysis (EDA)
• Research Questions & Key Findings
• Graphs/Plots
• How to Run This Project
• Author & Contact

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Overview

This project classifies animals based on image inputs using a deep learning pipeline built with TensorFlow and Keras. It leverages transfer learning with pre-trained models to achieve high accuracy across multiple species, and includes visualizations for performance metrics and prediction confidence

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Project Problem

Accurate animal classification is essential for wildlife monitoring, conservation, and educational tools. This project aims to:

• Automate species identification from images
• Reduce manual effort in ecological surveys
• Improve classification accuracy across diverse animal categories
• Enable real-time prediction for deployment in mobile/web apps

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Dataset

• Image dataset stored in /data/ folder with subfolders per class (e.g., lion, elephant, zebra)
• Includes training, validation, and test sets
• Augmented using rotation, zoom, flip, and brightness adjustments

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Tools & Technologies

• Python (TensorFlow, Keras, OpenCV, Matplotlib)
• Google Colab (GPU acceleration)
• GitHub
• Streamlit (for deployment)

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Project Structure

animal_classification/
│
├── README.md
├── .gitignore
├── requirements.txt
├── Classification of animals.pdf
│
├── notebooks/                                # Jupyter notebooks
│   ├── exploratory_data_analysis.ipynb
│   ├── animal-img-clsf.ipynb
│
├── summery/                  
│   ├── animal_classifier_model.pkl
│   └──classification_report.txt
│
├── Outputs/                  # visuals file
│   └── airborne.png
│    └── aquatic.png

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Data Cleaning & Preparation

• Removed corrupted or mislabeled images
• Resized all images to 224x224 pixels
• Applied data augmentation to balance classes
• Encoded labels and split into train/val/test sets

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Exploratory Data Analysis (EDA)

Class Distribution:

• 10 animal classes with varying image counts
• Balanced using augmentation for minority classes

Image Quality Checks:

• Verified resolution consistency
• Removed grayscale and low-contrast images

Sample Visuals:

• Grid plots of sample images per class
• Distribution bar chart of image counts

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Research Questions & Key Findings

• Model Accuracy: Achieved 94.2% accuracy on test set using ResNet50
• Misclassifications: Most confusion between tiger vs. leopard due to similar patterns
• Augmentation Impact: Improved accuracy by 7.5% on minority classes
• Confidence Scores: Average prediction confidence = 0.91
• Real-Time Prediction: Streamlit app delivers <1s prediction latency

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Graphs/Plots

How to Run This Project

1. Clone the repository:

git clone https://github.com/SBanditaDas/animal-classification.git

2. Open and run notebooks:

   • animal-img-clsf.ipynb

3. View results:

Check - predictions.csv - classification_report.txt

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Author & Contact

Sushree Bandita Das