vfrog.ai

What is Vfrog and how does it work?

Vfrog is a Computer Vision Platform with Auto-Annotation that serves as your on-demand computer vision engineer. Upload your images, and our AI automatically detects and annotates objects. Then use our HALO (Human Assisted Labelling of Objects) system to refine annotations with an intuitive drag-and-drop interface, train custom models, and iterate to improve accuracy.

What is computer vision and what can it do?

Computer vision is a field of artificial intelligence that enables machines to interpret and understand visual information from images and video. It powers applications like quality inspection on factory lines, object detection in security systems, medical image analysis, retail shelf monitoring, license plate recognition, and agricultural crop monitoring. With platforms like vfrog, you can build and deploy custom computer vision models without needing machine learning expertise.

How do I build a computer vision model without ML expertise?

With vfrog, you describe what you want to detect in plain English, upload as few as 20 images, and the platform handles the rest. Our AI auto-labels 80% of your data, synthetic data generation fills gaps in your training set, and one-click training produces a production-ready model. You can deploy via API in minutes — no Python, no ML frameworks, no GPU configuration required.

What is auto-annotation in computer vision?

Auto-annotation is the process of using AI to automatically label objects in images, replacing tedious manual annotation work. vfrog's SSAT (Smart Super-fast Auto-Tagging) system auto-labels approximately 80% of your dataset. You then review and refine the remaining annotations using HALO (Human Assisted Labelling of Objects), dramatically reducing the time from raw images to trained model.

How much does a computer vision platform cost?

Traditional computer vision development typically requires hiring a specialist team costing $300,000+ per year. vfrog offers a self-serve alternative starting at $49/month (Starter plan with 1,000 credits), $99/month (Pro with 3,000 credits), or $299/month (Business with 12,000 credits). All plans include a 14-day free trial with 500 credits and no credit card required.

What is the difference between computer vision and foundation models like GPT-4V?

Foundation models (like GPT-4V or Gemini) are large, general-purpose models that can describe images but are not optimized for specific detection tasks. They are expensive to run, require cloud connectivity, and offer lower precision for specialized use cases. Task-specific computer vision models — like those trained on vfrog — achieve 95%+ accuracy on your exact use case, run at under 50ms latency on edge devices, and cost up to 75% less than foundation model API calls.

Can I deploy computer vision models on edge devices?

Yes. vfrog supports deployment to both cloud and edge environments. Edge deployment means your model runs directly on local hardware (cameras, industrial PCs, embedded devices) without sending data to the cloud. This enables sub-50ms latency, works offline, reduces bandwidth costs, and keeps sensitive visual data on-premises. vfrog's small, task-specific models are optimized for edge hardware.

How does the credit system work?

Vfrog uses a credit-based system for fair, usage-based pricing. Credits are consumed when you use our AI tools: Auto-Annotation (SSAT) costs 5.89 credits per image, Training costs 4.47 credits per image, Batch Inference costs 0.26 credits per image, and API calls cost 0.03 credits each. Credits roll over and expire after 3 months.