📦 Inventory Management with Machine Vision.

Welcome to the Inventory Management project! This project leverages a zero-shot model and machine vision to streamline and enhance inventory management processes. Implemented in a Jupyter Notebook, it provides a powerful tool for automatically managing and tracking inventory items with minimal human intervention.

🔍 Overview

The Inventory Management system uses advanced machine vision techniques and zero-shot learning models to automatically identify and categorize inventory items. This approach reduces the need for extensive training data and adapts to new inventory items without requiring retraining.

🎯 Features

• Zero-Shot Learning: Utilize a zero-shot model to classify inventory items with minimal training data.
• Machine Vision Integration: Employ machine vision for real-time identification and categorization of inventory.
• Interactive Jupyter Notebook: A user-friendly notebook interface for easy experimentation and deployment.
• Automated Inventory Tracking: Automatically update and track inventory levels based on visual input.

🚀 Getting Started

Prerequisites

Before running the notebook, ensure you have the following installed:

• Python 3.8+
• Jupyter Notebook (pip install notebook)
• Pandas (pip install pandas)
• OpenCV (pip install opencv-python)
• Transformers (pip install transformers)
• Torch (pip install torch)

Installation

1. Clone the repository:

   git clone https://github.com/Jimil1407/inventory_management.git
   cd inventory-management
   

2. Install dependencies:

   pip install -r requirements.txt
   

🛠️ Usage

1. Prepare Your Data:

   • Ensure you have images of your inventory items and any relevant metadata, I have attached the data I used in the repository although you can use your own data.

2. Run the Notebook:

   • Execute the cells in the Jupyter Notebook to load data, train the zero-shot model, and process images.

3. Manage Inventory:

   • The notebook will classify and track inventory items, providing real-time updates and insights.

📊 Results

The notebook will output visual and numerical data showing classified inventory items, updated stock levels, and other relevant metrics. The results are designed to help you efficiently manage and monitor your inventory with minimal manual intervention.

🧠 How it Works

• Zero-Shot Learning: The system uses a pre-trained zero-shot model to classify new inventory items without requiring retraining.
• Machine Vision: Integrated with OpenCV to process and analyze images of inventory items in real-time.
• Data Handling: Utilizes Pandas for efficient data management and processing within the Jupyter Notebook.

🤝 Contributing

Contributions are welcome! If you have suggestions for improvements or new features, please open an issue or submit a pull request.