Pinpoint Issues as They Happen
Whether identifying package theft or monitoring chemical spills, computer vision is adept at detecting objects of various sizes.
Theft and Loss Prevention
Slip and Fall Hazards
Package Theft
Abandoned Luggage
Create a fully trained model in hours, not weeks.
AiPanthers interacts seamlessly with leading labeling systems and cutting-edge training solutions, optimizing your AI workflows for peak efficiency.
Use Your Existing Video Feeds for More Insights
If your security cameras and surveillance equipment record real-time video, you already have enough data to train a custom computer vision model. Begin using the footage you're collecting to gain important insights and improve your security operations.
Real-time Object Detection in Frame
In risk monitoring and danger detection, every second counts. A powerful computer vision model can recognize things quickly and precisely, allowing you to save time and money by automating these critical processes.
Improve Your Model Continuously After Deployment.
As a unified administration platform for your datasets, AiPanthers allows you to deploy your model once and scale it easily as your data increases. Detect new objects seamlessly, fine-tune setups, and experiment with alternative labeling and training options.
Improve model quality with fewer data points.
AiPanthers is specifically developed to enhance the value of data that you've already gathered.
Static Crop
If activity is limited to a specific area of the camera frame, cropping your photos to focus just on these active areas can drastically reduce training time by eliminating needless pixels.
Augmentations
Improve your source photographs by imitating different lighting conditions, camera angles, and contrast settings. This allows you to produce more training data without having to upload new photographs.
Version Control
Dataset variations allow for fast testing with various labeling and training approaches. Simply change your options, generate the export, and proceed.
Advanced Health Check
Informative graphics, such as class balance analysis, dimension information, and an annotation heatmap, provide insights into the quality of your dataset and places for improvement.