{"id":25423244,"url":"https://github.com/arpanpramanik2003/fruit-veg-classification","last_synced_at":"2026-04-18T15:34:17.730Z","repository":{"id":277033185,"uuid":"931113590","full_name":"arpanpramanik2003/fruit-veg-classification","owner":"arpanpramanik2003","description":"This project is a deep learning-based image classification system that uses EfficientNet for accurate identification of fruits and vegetables from uploaded images. 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The model is built using **TensorFlow** and deployed using **Streamlit**. The current model uses **EfficientNet** for improved accuracy and performance.\n\n## 🖥️ Tech Stack\n- **Python** (Core programming language)\n- **TensorFlow/Keras** (For model training and inference)\n- **EfficientNet** (Pre-trained model for feature extraction and classification)\n- **Streamlit** (For building the web UI)\n- **NumPy \u0026 PIL** (For image preprocessing)\n- **Matplotlib** (For visualization)\n\n## 🎯 Features\n- Upload an image of a fruit or vegetable\n- Predict the class with confidence score\n- Display confidence scores as a bar chart\n- User-friendly and interactive interface\n- Utilizes **EfficientNet** for high-accuracy predictions\n\n## 🏗️ Project Structure\n```\nfruit_veg_classifier/\n├── efficient_model.h5       # Trained EfficientNet model\n│── app.py                   # Streamlit web app script\n│── requirements.txt         # Dependencies for the project\n│── README.md                # Project documentation\n│── image.png                # Project preview image\n```\n\n## 🚀 Installation \u0026 Usage\n### 1️⃣ Clone the Repository\n```sh\ngit clone https://github.com/arpanpramanik2003/fruit-veg-classification.git\ncd fruit-veg-classification\n```\n\n### 2️⃣ Install Dependencies\n```sh\npip install -r requirements.txt\n```\n\n### 3️⃣ Run the Streamlit App\n```sh\nstreamlit run app.py\n```\n\n## 📷 Model \u0026 Image Preprocessing\n- The model uses **EfficientNet** as the backbone for feature extraction.\n- Input images are resized to **224x224 pixels** before inference.\n- The model has achieved high accuracy during training and testing.\n\n## 📊 Prediction Output\n- **Class Label:** Name of the detected fruit/vegetable\n- **Confidence Score:** Probability of prediction accuracy\n- **Bar Chart:** Visualization of class probabilities\n\n## 🛠️ Future Improvements\n- Enhance accuracy further with data augmentation\n- Deploy on reliable cloud platforms with minimal latency\n- Add support for more categories and datasets\n\n## 📜 License\nThis project is open-source and available under the **MIT License**.\n\n---\n📌 **Developed by Arpan Pramanik** | 💡 AI/ML Enthusiast 🚀\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farpanpramanik2003%2Ffruit-veg-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farpanpramanik2003%2Ffruit-veg-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farpanpramanik2003%2Ffruit-veg-classification/lists"}