Visual Prompting
Building computer vision systems in minutes via natural prompting interactions
Conventional AI Workflow
Requires multiple steps: (i) finding and labeling data, (ii) training a model, and then (iii) making predictions
Label→ Train → Predict (taking days/months)
BETA
Visual Prompting
Visual Prompting as a fast, easy and natural way to have a
vision-based interaction
Prompt → Predict (taking minutes/seconds)
Computer Vision Cloud Platform
Super easy
An intuitive platform that lets you create a custom computer vision project in minutes.
Accurate results fast
Data-Centric AI makes models work even with small datasets by ensuring data quality.
Flexible deployment
Cloud and edge device deployment options make integration into existing environments easy. Deploy your model with just a few mouse clicks.
Unlimited scalability
From a single production line to worldwide operations, LandingLens makes scaling projects simple.
Computer Vision For All Industries
How It Works
Upload images
- Drag and drop images directly into LandingLens.
- Capture live images from your webcam.
- You can also upload labeled images so you can start training your model faster.
Label objects in your images
- AI models are only as good as the data you feed them. However, labeling datasets can be an arduous and time-consuming task.
- The LandingLens labeling tool quickly helps you create high-quality training data that your AI model can use for maximum accuracy.
Train your model and evaluate its performance
- When you train your model, LandingLens learns what to look for based on your labeled images.
- LandingLens also automatically runs label checking in the background. If it is confident that an image’s labels can be improved, it will let you know.
- After completing model training, you can test your model performance by showing it new images.
View your model’s predictions and deploy
- Once your model is accurate enough for use, deploy it to the cloud or edge devices.
- You can monitor your model performance and update your model as needed.