Animals Image Classification

A collection of convolutional neural network (CNN) models for classifying images of various animal categories. The project supports:

• Marine Species (20 classes)
• Reptiles & Amphibians (10 classes)
• Birds (15 classes)
• Wild Mammals (21 classes)
• Category-Agnostic model for unknown images

All category-specific classifiers are built with TensorFlow and deployed via a Django web application.

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📋 Table of Contents

1. Features
2. Model Architecture
3. Data & Classes
4. Installation
5. Usage
6. Project Structure
7. Screenshots
8. Contact

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🔥 Features

• High-Accuracy Classification – Marine, reptile/amphibian, bird & wild-mammal models exceed 90% accuracy on held-out test sets.
• Category-Agnostic Fallback – A standalone CNN that guesses the correct animal category when the type is unknown.
• Web Interface – Image upload & real-time prediction via Django.
• Modular Design – Separate models & endpoints for each category.

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🏗️ Model Architecture

Category	Base CNN	Description
Marine Species (20)	ResNet-101	Pretrained on ImageNet, fine-tuned on marine dataset
Reptiles & Amphibians (10)	ResNet-101	Same backbone, different class head
Wild Mammals (15)	ResNet-101	Same backbone, different class head
Birds (15)	MobileNetV2	Lightweight model optimized for bird images
Category-Agnostic	Custom CNN	Built from scratch to recognize any animal category

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📊 Data & Classes

• Marine Species (20): – Examples: dolphin, shark, sea turtle, clownfish, manta ray… – Dataset used: Marine Animal Dataset

• Reptiles & Amphibians (10): – Examples: crocodile, gecko, frog, salamander, iguana… – Dataset used: Reptile & Amphibian Dataset

• Birds (15): – Examples: sparrow, eagle, parrot, penguin, flamingo… – Dataset used: Bird Species Dataset

• Wild Mammals (21): – Examples: lion, tiger, elephant, bear, giraffe… – Dataset used: Wild Mammals Dataset

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🚀 Installation

1. Clone the repository

   git clone https://github.com/yourusername/animals-image-classification.git
   cd animals-image-classification

2. Create & activate a virtual environment

   python3 -m venv venv
   source venv/bin/activate

3. Install Python dependencies

   pip install -r requirements.txt

4. Apply migrations & collect static files

   python manage.py migrate
   python manage.py collectstatic

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🎯 Usage

1. Train a model : Execute the Jupyter Notebooks located in the notebook directory to get the models

2. Start the Django development server

   python manage.py runserver

   Open http://localhost:8000/ in your browser.

3. Upload & classify an image

   • Select the appropriate category or choose “Click here”
   • View predicted label

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📂 Project Structure

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├── notebook/                   # Jupyter notebooks for building and training models
├── interface/                  # Django project settings and configuration
│   ├── __init__.py
│   ├── settings.py
│   ├── urls.py
│   ├── wsgi.py
│   └── asgi.py                # Standard Django project files
├── page/                       # Django app for classification UI and logic
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── forms.py
│   ├── models.py
│   ├── predict.py             # Model inference logic
│   ├── tests.py
│   ├── urls.py
│   ├── views.py
│   └── static/                # Static assets (CSS, JS, images)
├── templates/                  # HTML templates for each category and index
│   ├── index.html
│   ├── marine.html
│   ├── reptiles.html
│   ├── birds.html
│   ├── wild.html
│   └── unknown.html
├── requirements.txt            # Python dependencies
└── README.md                   # Project overview and instructions

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🖼️ Screenshots

Below are sample screenshots of the web interface in action:

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📬 Contact

💼 LinkedIn
🐙 GitHub