SYNETIC.ai
Research & Resources
Peer-reviewed research, technical whitepapers, and educational resources on synthetic training data for computer vision.
Peer-Reviewed Research
Independently validated, published findings
Synetic Trained Model
Detects all apples, including those missed by humans
Better Than Real: Synthetic Apple Detection for Orchards
USC Reviewed | November 2025
Authors
Synetic AI: Octavian Blaga, David Scott
University of South Carolina: Dr. Ramtin Zand, James Blake Seekings
Training computer vision models on 100% synthetic data outperformed real-world datasets by 34%. Independently validated by the University of South Carolina across 7 different YOLO architectures. Hybrid approaches (mixing synthetic + real) actually reduced performance by 10-15%.
Technical Whitepapers
In-depth analysis and platform comparisons
Synetic vs Omniverse
Comparison
Technical
Comprehensive evaluation of Synetic AI and NVIDIA Omniverse as platforms for generating synthetic data for computer vision, focusing on workflow simplicity, scalability, and dataset utility.
Small Purpose Built AI Models
Architecture
Edge AI
In an era dominated by ever-larger foundation models, this paper argues that the most impactful AI systems will be compact, efficient models tailored to specific tasks with embedded business logic.
Educational Resources
Learn about synthetic data and computer vision
Synthetic Data for Computer Vision
Educational
Overview
Synthetic data is changing how computer vision models are trained. Learn what synthetic data is, how it compares to traditional approaches, and how to evaluate the best synthetic data source for your project.
Learn About Computer Vision
Education
Getting Started
Comprehensive introduction to computer vision and how Synetic.ai’s automated platform generates photorealistic, annotated, and customizable training datasets that are revolutionizing the field.
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