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In an era dominated by ever-larger foundation models, a quieter revolution is underway—one defined not by scale, but by precision. This paper argues that the most impactful AI systems of the future will not be the largest, but the smallest: compact, efficient models tailored to specific tasks. By embedding business logic directly into the neural architecture, these purpose-built models can outperform their bloated counterparts in real-world deployments, especially at the edge.

This paper evaluates Synetic AI and NVIDIA Omniverse as platforms for generating synthetic data for computer vision, focusing on workflow simplicity, scalability, and dataset utility.

Synthetic data is revolutionizing computer vision. Synetic.ai provides an automated platform for generating training datasets that are photorealistic, annotated, and customizable.

Synthetic data is changing how computer vision models are being trained. This page will explain synthetic data and how it compares to traditional approaches. After exploring the main methods of creating synthetic data, we’ll help you evaluate and choose the most effective synthetic data source for your project.