CNN-Powered Computer Vision System

Discover the
Intelligence of Wings.

An intelligent butterfly species identification system powered by Deep Learning, Computer Vision and a custom Convolutional Neural Network.

Try Live Demo → Explore Architecture
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PROJECT OVERVIEW

AI meets nature.

Butterfly species often share similar colors, patterns and shapes, making manual identification difficult. This project uses Deep Learning to analyze butterfly images and predict their species automatically.

75
Butterfly Species
6.5K+
Training Images
128×128
Image Resolution
Top 5
AI Predictions
🔍

The Challenge

Identifying butterfly species manually requires knowledge of wing structure, colors, patterns and subtle visual characteristics.

🧠

The AI Solution

A Convolutional Neural Network learns visual features directly from thousands of butterfly images and predicts the most likely species.

AI WORKFLOW

From image to intelligence.

The system follows a complete machine learning pipeline, transforming a butterfly image into an intelligent species prediction.

🖼️

Input Image

User uploads a butterfly image.

⚙️

Preprocessing

Resize and prepare image data.

🧠

CNN Analysis

Extract important visual features.

📊

Classification

Compare features across 75 classes.

🦋

Prediction

Return the top predicted species.

SUPPORTED SPECIES

75 Butterfly Species.

The AI model has been trained to recognize and classify 75 different butterfly species.

MODEL ARCHITECTURE

Inside the CNN.

A custom Convolutional Neural Network processes butterfly images through multiple feature extraction layers before generating the final species classification.

INPUT 128 × 128 × 3
→
CONV BLOCK 1 Feature Extraction
→
CONV BLOCK 2 Pattern Learning
→
CONV BLOCK 3 Deep Features
→
CONV BLOCK 4 High-Level Features
→
OUTPUT 75 Species
INTERACTIVE DEMO

Test the AI.

Upload a butterfly image to experience the interface of the AI classification system.

Uploaded Butterfly
🦋

Upload Butterfly Image

Drag & drop an image here or select a file.

AI Prediction Result
MONARCH
94.73% Confidence
MONARCH 94.73%
VICEROY 3.12%
PAINTED LADY 1.24%
RED ADMIRAL 0.61%
MALACHITE 0.30%
TRAINING PROCESS

Teaching the model to see.

01

Dataset Preparation

Thousands of butterfly images are organized into multiple species classes for supervised learning.

02

Data Augmentation

Image transformations such as rotation, flipping and zooming help the model learn more robust visual features.

03

CNN Training

The custom CNN learns hierarchical visual patterns from butterfly wings and body structures.

04

Model Evaluation

The trained model is evaluated to understand how well it performs on unseen butterfly images.

05

Deployment

The trained model is integrated with a Flask API and connected to an interactive web interface.

BACKEND SYSTEM

Flask API integration.

The frontend communicates with the trained AI model through REST API endpoints.

API ENDPOINTS
GET
/
Main web application interface
GET
/api/status
Check model status
POST
/api/predict
Upload an image and predict butterfly species
GET
/api/species
Retrieve all supported butterfly species
TECHNOLOGY STACK

Built with modern AI tools.

🐍

Python

🧠

TensorFlow

👁️

Computer Vision

🌐

Flask API

📊

CNN

💻

JavaScript

🎨

HTML & CSS

📓

Jupyter