A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models

Selecting an appropriate semantic segmentation model for a given application domain remains a challenging and time-consuming task for practitioners and researchers. This paper presents an interactive, web-based platform that enables side-by-side visual comparison of multiple neural network segmentation models applied to identical images. The system integrates three transformer-based segmentation models: a face-parsing network producing 19 semantic classes, a SegFormer-B3 clothing segmentation model with 18 classes, and a Mask2Former model for general-purpose scene segmentation spanning 150 ADE20K categories. Key contributions include side-by-side evaluation of model outputs across multiple architectures and image categories, with real-time segment highlighting and a scalable inference caching system that enables model comparisons without requiring repeated graphics processing unit (GPU) computation. The platform organizes a curated dataset of images under a hierarchical category taxonomy, supporting structured evaluation across demographic and contextual variables. As a practical use case, the system is applied within the ADRIAN project to assist in verifying identity consistency across images through segmentation-based analysis. The platform thus contributes a specialized artificial intelligence (AI) tool for systematic segmentation and object detection model evaluation within media analysis pipelines, where selecting appropriate models is a recurring challenge across tasks from identity verification to content moderation. It is available under https://github.com/vika-v-v/neural-networks-for-image-segmentation and designed to lower the barrier for comparative model evaluation in applied computer vision workflows.