Dataset Link

📌 Project Overview

This project is a deep learning-based image classification system that identifies different types of fruits and vegetables from an uploaded image. The model is built using TensorFlow and deployed using Streamlit. The current model uses EfficientNet for improved accuracy and performance.

🖥️ Tech Stack

• Python (Core programming language)
• TensorFlow/Keras (For model training and inference)
• EfficientNet (Pre-trained model for feature extraction and classification)
• Streamlit (For building the web UI)
• NumPy & PIL (For image preprocessing)
• Matplotlib (For visualization)

🎯 Features

• Upload an image of a fruit or vegetable
• Predict the class with confidence score
• Display confidence scores as a bar chart
• User-friendly and interactive interface
• Utilizes EfficientNet for high-accuracy predictions

🏗️ Project Structure

fruit_veg_classifier/
├── efficient_model.h5       # Trained EfficientNet model
│── app.py                   # Streamlit web app script
│── requirements.txt         # Dependencies for the project
│── README.md                # Project documentation
│── image.png                # Project preview image

🚀 Installation & Usage

1️⃣ Clone the Repository

git clone https://github.com/arpanpramanik2003/fruit-veg-classification.git
cd fruit-veg-classification

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Streamlit App

streamlit run app.py

📷 Model & Image Preprocessing

• The model uses EfficientNet as the backbone for feature extraction.
• Input images are resized to 224x224 pixels before inference.
• The model has achieved high accuracy during training and testing.

📊 Prediction Output

• Class Label: Name of the detected fruit/vegetable
• Confidence Score: Probability of prediction accuracy
• Bar Chart: Visualization of class probabilities

🛠️ Future Improvements

• Enhance accuracy further with data augmentation
• Deploy on reliable cloud platforms with minimal latency
• Add support for more categories and datasets

📜 License

This project is open-source and available under the MIT License.

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📌 Developed by Arpan Pramanik | 💡 AI/ML Enthusiast 🚀