MetaRetail AI – Vision Transformer-Based Autonomous Retail Analytics System was inspired by the need to make retail stores smarter and more efficient using Artificial Intelligence. Traditional retail monitoring mainly depends on manual observation, which can be time-consuming and may miss important activities. Our goal was to develop an intelligent system that can automatically understand retail environments through computer vision.
We built the project using Vision Transformer (ViT) technology to analyze images and video captured from retail environments. The system follows a pipeline of camera input → image preprocessing → AI-based visual analysis → retail insights → dashboard. It can be extended to support customer activity analysis, product monitoring, crowd detection, and unusual-event identification.
During development, we learned about computer vision, dataset preparation, image preprocessing, Vision Transformer architecture, model evaluation, and real-time AI analytics. We also learned how AI models can be integrated with software applications to provide useful information to users.
One of the main challenges was handling different lighting conditions, camera angles, crowded scenes, and variations in customer movement. Another challenge was balancing model accuracy with real-time performance and computational requirements.
Through Meta retail AI, we aim to demonstrate how AI-powered visual analytics can help transform conventional retail monitoring into a smarter, automated, and data-driven system.
Built With
- colab
- css
- dashboard
- frontend
- javascript
- jupyter
- model
- python-?-ai/ml-development-vision-transformer-(vit)-?-visual-analysis-opencv-?-image-and-video-processing-pytorch-?-deep-learning-model-development-numpy-&-pandas-?-data-processing-fastapi-?-backend/api-html
- training
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