Custom computer vision
models for any use case
Train private, task-specific, small-vision models that are faster, smarter, and less expensive than Large Vision Models — no ML required.
14-day free trial · No credit card · Cancel anytime
Product recognition & shelf analytics
Higher accuracy than foundational models at a fraction of the cost
The fastest path from idea to production ready computer vision models.
95% Accuracy
Built for your data, not everyone else's. Our models learn exactly what you need to detect - nothing more, nothing less.
75% Cheaper Than Foundation Models
Small vision models train in hours, not weeks. And their compact size means you can run them on affordable hardware without the cloud bill.
Low Latency
Under 50ms on edge, under 300ms on cloud. Fast enough for any application, anywhere.
Cloud or Edge
Deploy wherever makes sense for your business. Small model size means you're not locked into expensive infrastructure.
Free Image Annotation Tool
Draw bounding boxes, polygons, and segmentation masks — then export as YOLO, JSON, or COCO. Free forever, no credit card required.
A custom model for any use case
We leverage your data to create specialized models with high accuracy where it matters.
Manufacturing & Industry
- Quality Control: Automated inspection systems detect product defects, such as broken tablets in pharmaceutical packaging, damaged parts on conveyor belts, or incorrect assembly.
- Predictive Maintenance: Cameras monitor machinery, using visual data to predict equipment failure before it happens.
- Robotic Guidance: Robotic arms use computer vision to identify, pick, and position components on assembly lines.
Agriculture & Environmental Monitoring
- Crop and Soil Health: Drones and cameras monitor crop growth, disease, and pest infestation for targeted pesticide use.
- Harvesting Robots: Automated systems identify and pick ripe produce.
- Wildlife Monitoring: Tracking animal behavior for conservation efforts.
Transportation & Automotive
- Autonomous Vehicles: Self-driving cars use cameras to interpret traffic signs, detect pedestrians, recognize lane markings, and avoid collisions.
- Traffic Monitoring: Analyzing traffic flow to optimize signal timing and manage congestion.
- License Plate Recognition: Automated systems for parking, tolls, and security.
Retail & E-commerce
- Automated Checkout: Systems, such as "Just Walk Out" technology, allow shoppers to pay without scanning items.
- Inventory Management: Cameras monitor shelves to track stock levels and alert staff for restocking.
- Customer Analytics: Tracking foot traffic, dwell times, and behavior to optimize store layouts.
Security & Safety
- Facial Recognition: Identifying authorized personnel or flagging individuals in security surveillance.
- Employee Safety: Monitoring workplaces to ensure compliance with safety protocols and detect hazards.
- Anomaly Detection: Identifying suspicious activity, such as loitering or theft, and detecting fire or smoke.
Healthcare & Medical Imaging
- Diagnostic Imaging: Algorithms analyze MRI scans, X-rays, and CT scans to detect diseases like cancer or pneumonia, supporting radiologists in diagnosis.
- Retinal Screening: Identifying abnormalities in eye images to detect diseases.
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.
Do you offer a free trial?
Yes! We offer a 14-day free trial with 500 credits to test the platform. No payment is required upfront. If you don't subscribe after the trial ends, your trial credits will be withdrawn.
What if I run out of credits?
You can purchase credit packs anytime: Small ($25 for 400 credits), Medium ($75 for 1,500 credits), or Large ($200 for 5,000 credits). Credit packs are available to organizations with active subscriptions and credits are added instantly upon purchase.
What kind of support do you provide?
We offer multiple support channels to help you succeed. Chat with Kermit, our AI assistant, for instant help anytime. You can also reach our team via email support. Business plan include priority support, while Enterprise customers receive dedicated account management.
Frequently Asked Questions
Everything you need to know about Vfrog. Can't find what you're looking for? Contact us
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