🍊 Introducing Citrus-Net

An end-to-end image classification pipeline that combines 🧱 Custom CNN, 🔁 Transfer Learning, and 🔭 Vision Transformer models — enhanced with 🧩 Explainable AI (XAI) visualizations and deployed in a 🌐 Streamlit app.

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📌 Repository Description

This repository contains the complete project for building and explaining a full image classification pipeline. The pipeline moves from a hand-crafted CNN baseline to state-of-the-art deep learning models, enriched with Explainable AI (XAI) methods, and deployed as an interactive Streamlit web app.

🚀 Project Stages

• 🔎 Data Preparation – Exploratory analysis, class balance checks, preprocessing, and augmentations.
• 🧱 Custom CNN – Novel convolutional network designed and trained from scratch.
• 🔁 Transfer Learning – Fine-tuning four pretrained CNN backbones (ImageNet weights).
• 🔭 Vision Transformer (ViT) – Transformer-based classifier for advanced performance.
• 🧩 XAI (Explainability) – Applying Grad-CAM, Score-CAM, LIME, and SHAP to interpret predictions.
• 🌐 Streamlit App – User-friendly interface for image upload, prediction, and interactive XAI visualizations.

✅ Key Features

• 🧱 Custom CNN with ≥65% accuracy (baseline target).
• 🔁 Transfer learning with four unique CNN architectures (not from demo code).
• 🔭 Vision Transformer experiment.
• 📊 Comparative evaluation (accuracy, precision, recall, F1, confusion matrices).
• 🧩 XAI overlays on ≥10 test images with detailed interpretation.
• 🌐 Streamlit app for model selection, predictions, and side-by-side explanations.

🛠️ Tech Stack

• Python (PyTorch, torchvision, scikit-learn, Captum, pytorch-grad-cam, LIME)
• Streamlit for deployment
• Matplotlib / Seaborn for plots
• Google Colab / Kaggle GPU for training

📂 Dataset

We used the 🍊 Citrus Fruit Dataset containing high-quality images of citrus fruits for training, validation, and testing.

📑 Deliverables

• 📦 Public GitHub repo with runnable code.
• 💾 Trained model weights (via Google Drive link after contacting).
• 📄 Scientific project report (IEEE/ACM style).
• 🌐 Streamlit demo folder.

🚀 Quick Start

1️⃣ Clone the Repository

git clone https://github.com/your-username/Citrus-Net.git
cd Citrus-Net

2️⃣ Install Requirements

pip install -r requirements.txt

3️⃣ Run the Streamlit App

streamlit run app.py

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📸 XAI Heatmap

(Generated using Grad-CAM & LIME on random test images)

📧 Contact

If you need the trained models or datasets for academic purposes, feel free to Contact Anytime.

⭐ Acknowledgements

• Built with PyTorch, torchvision, scikit-learn, pytorch-grad-cam, LIME, and Streamlit.
• Inspired by cutting-edge research in Computer Vision & Explainable AI (XAI).

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