Landing.ai

Description

how to use:
To use LandingLens, users can upload images by dragging and dropping them directly into the platform or capturing live images from their webcam. They can label objects in the images using the LandingLens labeling tool, which helps create high-quality training data for maximum accuracy. After labeling, users can train their model and evaluate its performance. Once the model is accurate enough, it can be deployed to the cloud or edge devices with just a few mouse clicks. Users can monitor the model’s performance and update it as needed.
Core freatures:
Visual Prompting: Fast and easy way to have vision-based interactions by prompting the systemData-Centric AI: Ensures models work even with small datasets by ensuring data qualityFlexible Deployment: Cloud and edge device deployment options to integrate into existing environmentsUnlimited Scalability: Allows scaling projects from a single production line to worldwide operations
Use case:

Automotive: Quality inspection, defect detection, and autonomous driving

Electronics: Quality control, PCB inspection, and component recognition

Food & Beverage: Quality assurance, contamination detection, and package inspection

Medical Devices: Defect identification, product verification, and quality control

Life Sciences: Cell identification, drug discovery, and research analysis

Agriculture: Crop monitoring, disease detection, and yield optimization

Manufacturing: Production line monitoring, defect detection, and process optimization

Infrastructure: Facility management, safety monitoring, and object recognition

Pharma: Drug packaging verification, counterfeit detection, and quality control

EV Manufacturing: Battery inspection, component recognition, and production optimization

FAQ list: