Stop annotating. Start generalizing.
No-gen synthetic images
87% of Computer Vision projects never reach production
Real data struggles to represent all the variation and use cases that allows models to generalize after deployment.
Synetic.ai data represents infinitely more use cases at a fraction of the time and cost.
How Synetic.ai works
1
Define what you want to recognize
Start with a simple prompt or description: the object, behavior, or condition you want your vision system to detect.
2
Generate rendered data
Our platform builds photorealistic, physics-accurate datasets using advanced rendering and simulation. Every image is perfectly annotated — no manual labeling required.
3
Customize your variables
Specify conditions like lighting, camera angle, occlusion, or motion blur. Synetic.ai gives you full control over every parameter so your model sees the real-world variation it will face.
4
Train your model
Select your preferred architecture (YOLO, RT-DETR, DINOv2, and more) and train directly in the platform with pre-tuned hyperparameters or your own custom settings.
5
Test and validate
Upload your own images or video to test model performance instantly. Synetic closes the loop with a supervised learning system that updates models based on edge cases.
Case Study
CropSight: Computer Vision for crops
Challenge
Computer vision in agriculture is hard. Seasonal grow cycles, shifting light, and unpredictable field conditions make real-world data slow and costly to collect.
Key Features
Stand count & emergence tracking
Solution
CropSight combines pivot-mounted cameras with Synetic AI’s rendered datasets, delivering full-field coverage and models trained on every growth stage without waiting for seasonal data.
Impact
CropSight cuts data collection from months to days, achieves near-perfect field coverage, and gives growers real-time insights into emergence, irrigation, and plant health.
No-gen synthetic image
Real-world generalized image
Build computer vision models that outperform real data.
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