Revolutionises Online Shopping with Advanced Logo Detection Technology
Independent researcher Mitesh Sinha shared insights on his AI-powered solution that automates logo detection in garments, enhancing efficiency and accuracy for second-hand clothing marketplaces. He detailed how the innovation addresses key operational challenges in e-commerce
In the fast-paced world of e-commerce, where brand identity plays a pivotal role in driving consumer trust and sales, accurately identifying logos on garments has become increasingly important—especially in the burgeoning market of second-hand clothing. Recognising the inefficiencies and limitations of traditional manual logo detection, independent researcher Mitesh Sinha has stepped forward with a breakthrough: an AI-driven solution that significantly enhances both the speed and accuracy of brand identification.
“Manual sorting has always been a bottleneck,” Sinha explained. “It’s labor-intensive, prone to error, and simply not scalable when you’re dealing with hundreds of thousands of garments, as companies like Sellpy do.” With a vision to automate this tedious process, Sinha developed a system that leverages artificial intelligence to detect logos using a blend of visual and textual cues.
Trained on a robust dataset of over 204,000 images across 3,200+ brand categories sourced from Sellpy, the model is engineered to recognise even subtle branding elements on apparel. “What sets this apart is the integration of deep learning with a domain-specific focus,” Sinha shared. “Rather than relying on just one form of recognition, the model uses a combination of visual features and any textual signals available in the image.”
The results speak for themselves. The AI system demonstrated a 99% precision rate for Adidas and 89% for Nike—figures that significantly outperform traditional manual methods. “These numbers validate not just the model’s capabilities but also the need for automation in this space,” said Sinha.
Yet, the project also unveiled limitations, particularly with lesser-known brands that had limited data representation. “The long tail is always a challenge,” Sinha admitted. “But the architecture is designed to be adaptable. As more data becomes available, the model can evolve.”
Beyond efficiency, Sinha believes the technology’s real value lies in consistency and scalability. “Retailers can now sort and list products faster, reduce operational costs, and improve the accuracy of listings—all of which enhance customer trust,” he noted. In an era where online shoppers increasingly expect seamless and reliable experiences, these improvements carry significant weight.
Sinha’s innovation is already gaining attention across the e-commerce landscape, setting a new benchmark for what’s possible with AI in retail. “This isn’t just a technical upgrade—it’s a transformation in how businesses handle inventory at scale,” he emphasised.
As the second-hand clothing market continues to expand, Mitesh Sinha’s work offers a timely and impactful solution—demonstrating the power of artificial intelligence to tackle real-world business challenges and reshape the future of fashion retail.
