Vision-based Models

Vision-based Models are deep learning architectures (e.g., CNNs, Vision Transformers) engineered to process and interpret visual data, driving tasks like object detection and image classification.

Vision-based Models are the core engine for modern Computer Vision, utilizing specialized neural networks to extract meaning from images and video. Architectures like the Convolutional Neural Network (CNN) and the more recent Vision Transformer (ViT) are foundational. They power critical applications: Object Detection (YOLO models) for autonomous vehicles, image classification (ResNet on ImageNet) for content tagging, and multimodal reasoning (CLIP) for aligning text and visuals. These models are deployed across industries, automating quality control, enabling facial recognition, and providing real-time visual analytics at scale.

https://cloud.google.com/vision