Image recognition AI works by analyzing visual data to identify objects, patterns, or features. It uses convolutional neural networks (CNNs) to extract features hierarchically, starting from basic elements like edges to more complex structures like objects or scenes.
During training, the AI model learns to associate features with labels using large datasets. Once trained, it processes new images by applying the learned patterns, outputting predictions like classifications or bounding boxes.
Image recognition AI powers applications such as facial recognition, medical diagnostics, and product recommendations, demonstrating its versatility across domains.