Shoe Ranger is a cutting-edge AI platform designed to assist event organizers, athletic brands, and data analysts by identifying and classifying footwear from large-scale image datasets, particularly in events like marathons or sports meets.
Artificial Intelligence, Event Tech, Sports Analytics
Shoe Ranger simplifies and automates the complex task of identifying the brand and gender classification of running shoes from thousands of event photographs. Built for scenarios with high image volumes, the platform utilizes AI and computer vision to classify shoes, remove duplicate images, and generate detailed insights with minimal manual input. It includes a feedback mechanism where admins or employees can correct AI predictions to improve the model’s accuracy over time. The system provides a powerful admin console, role-based access, and seamless image handling capabilities.
The development focused on scalability, performance, and accuracy. The team prioritized building a robust AI model capable of recognizing shoe brands and gender categories with high precision. A user-friendly interface allowed admins and employees to upload massive image sets, validate outputs, and engage in continuous model training through a human-in-the-loop framework. Deduplication and performance tuning were essential to ensuring real-time responsiveness, even under large-scale image loads. The result was a scalable, cloud-based platform that could deliver actionable insights with minimal delay.
Shoe Ranger significantly reduced the time and manpower required to analyze marathon event photos. With over 95% classification accuracy achieved through iterative training and feedback, event organizers and brand partners were able to derive meaningful analytics on footwear usage and preferences. The platform enabled quick decision-making, optimized marketing strategies, and improved event data organization. Shoe Ranger has proven to be a reliable and intelligent solution for mass image analysis in athletic and event-driven industries.
