Inception-v3

A deep convolutional neural network architecture developed by Google for image recognition, introduced in 2015. It uses "inception modules" that apply multiple convolution filter sizes in parallel to efficiently capture features at different scales, balancing recognition accuracy with computational speed. In accessibility contexts, Inception-v3 is commonly used as the backbone for personalized object recognition systems for blind users — its pre-trained weights can be fine-tuned via transfer learning on a small set of user photos, enabling on-device or server-side recognition without requiring massive training datasets.