EX Squared’s AI-based solution generates standardized image labels, unique image metadata, and enables personalized image search for increased UX, engagement, and SEO organic traffic.
To ensure standardized metadata, we analyzed the data input points and defined criteria to differentiate labels and objects. This was crucial for consistency across multiple annotators.
In addition to classifying images with confidence scoring and identifying objects within each image, our solution also added metadata for OCR content, SEO, and a proprietary aesthetic quality score to help promote “better looking” images above others.
Models were trained using the Caffe and TensorFlow deep-learning frameworks; PyTorch is also an option. Our models achieved 97% accuracy within 8 major categories. TensorFlow Serving was used for API deployment.
Approximately 100,000 images (20,000 with objects) were classified, with around 40+ labels. Our quality control process prevented models from publishing unexpected outputs.
EX Squared’s AI image classifier accurately predicts and annotates real estate images with over 96% accuracy. It can detect 300+ objects within an image, and classify each image into 30+ categories and subcategories.
Centralized data improves targeting and follow up, while automated distribution keeps listings consistent on more than 50 partner sites, expanding reach and improving lead quality. The result is faster updates, fewer errors, higher engagement, and a smoother path to conversion.
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