• 

  Model Selection

• 

  Upload Bulk Images

• 

  Fine-Tunning

• 

  Show Inventory

• 

  Forecast Inventory

• 

  Bar Chart

• 

  Line Chart

• 

  Pie Chart

• 

  Heat Map

VisionTrack - Smart Inventory Management System

Inspiration

The inspiration for VisionTrack - Smart Inventory Management System came from the need to streamline and automate inventory management processes. Traditional inventory systems often face inefficiencies and inaccuracies, especially in large-scale operations. With this in mind, we aimed to build a solution that leverages advanced image classification and machine learning to enhance the efficiency, accuracy, and scalability of inventory management.

What it Does

VisionTrack is a state-of-the-art inventory management system designed to:

• Automatically classify and categorize inventory items using cutting-edge image recognition technology.
• Provide real-time insights and updates on inventory status to facilitate better decision-making.

App Features

Based on the judges' feedback, we have transformed VisionTrack into a more user-friendly and robust Inventory Management system.

1. Multi Image Recognition Model Selection

Choose from a range of advanced models for image recognition:

• Google ViT (Base): A powerful vision transformer model for efficient image classification.
• Google ViT (Large): A larger version of ViT, offering improved performance on complex tasks.
• Microsoft ResNet50: A deep convolutional network designed for image recognition tasks, known for its efficiency.
• Facebook ConvNeXt Tiny: A compact model optimized for fast inference without sacrificing accuracy.
• Microsoft Swin: A hierarchical vision transformer model that scales well across image sizes and tasks.

2. Fine-Tuning with Custom Data

Fine-tune the selected model to enhance its accuracy for your specific use case:

• Adjust hyperparameters to optimize model performance.
• Upload your own labeled dataset for fine-tuning, allowing the model to better understand your domain-specific images.

3. Bulk Image Classification

• Upload multiple images at once for batch processing.
• Classify a large set of images in one go and retrieve results efficiently.

4. Inventory Management

Keep track of the images you've uploaded and their corresponding classification results:

• View the classification labels and associated data for each image.
• Monitor the inventory status of your image dataset for easy access and management.

5. Forecasting using Machine Learning

• Use the classification results to predict future trends or behaviors.
• Forecast future inventory needs or category distributions based on historical classification data.

6. Inventory Analytics Dashboard

Visualize and analyze your inventory and classification results through various charts:

• Bar Chart: Display the distribution of classified items across categories.

• Pie Chart: Visualize the proportion of categories within your dataset.

• Line Chart: Track trends and patterns in your inventory over time.

• Heatmap: Explore the relationships between various attributes in your dataset and visualize patterns.

How We Built It

We built VisionTrack using a combination of modern technologies and frameworks:

• Frontend: The user interface was developed using Gradio, which allows for seamless image classification tasks through an intuitive, easy-to-use platform.
• Image Classification Models: Integrated a range of advanced image classification models, including:
  • Google ViT (Base)
  • Google ViT (Large)
  • Microsoft ResNet50
  • Facebook ConvNeXt Tiny
  • Google MobileNetV3
• Inventory Database: Created a robust database to store categorized images. This database also supports forecasting, predictive analytics, and overall analytics for inventory management.
• Custom Fine-Tuning: To allow for product-specific customization, we implemented a custom fine-tuning feature where users can upload their own labeled product images. They can then fine-tune the model to better classify their specific products and use the fine-tuned model for further classification tasks.

Challenges We Ran Into

1. Model Accuracy: Achieving high accuracy in classifying a wide variety of inventory items required fine-tuning and extensive testing of the Vision Transformer model.
2. System Integration: Integrating the image classification model with the Gradio interface and ensuring smooth communication between components posed significant challenges.
3. Scalability: Handling a large volume of images and ensuring system efficiency as the dataset grows was a concern.
4. Proxy Handling: Configuring the application to work properly behind a reverse proxy required setting up the right middleware and environment variables.

Accomplishments We're Proud Of

• Automated Classification: Successfully implemented an automated image classification system using the Vision Transformer model, enabling the accurate categorization of inventory items.
• Enhanced User Experience: Developed an intuitive and responsive user interface with Gradio, simplifying inventory management tasks for users.
• Seamless Deployment: Achieved consistent and reliable deployment using Docker, ensuring the application works seamlessly across different environments.

What We Learned

• Machine Learning Integration: Gained valuable insights into integrating machine learning models with web applications, especially in handling image data for classification tasks.
• Effective Use of Gradio: Learned how to leverage Gradio for building user-friendly interfaces and handle middleware configurations to ensure accurate request processing.
• Scalability and Performance: Identified key scalability challenges and implemented solutions to ensure efficient handling of large data volumes.

What's Next for VisionTrack

• Mobile Support: Develop mobile-friendly versions of the application to expand accessibility and usability across a wider range of devices.
• System Integrations: Explore potential integrations with other business systems such as ERP and CRM to provide a more comprehensive inventory management solution.
• User Feedback: Collect and analyze user feedback to drive iterative improvements, ensuring that the system continues to meet the evolving needs of its users.

Built With

• flask
• nvidia-workbench
• python