The Ultimate Guide to Creating Custom Object Detection Models with LensPath
Computer vision has unlocked countless possibilities for businesses to automate and optimize operations, from detecting objects to identifying people and events. However, creating custom object detection models often requires technical expertise and complex tools that many businesses find inaccessible. LensPath changes the game with its intuitive platform, enabling users to build, train, and deploy custom models with ease—no coding required.
In this guide, we’ll walk you through how to create custom object detection models using LensPath, tailored to your specific business needs.
Step 1: Define Your Object Detection Goals
Before building a model, you need to identify what you want to detect and why it matters for your workflows.
Questions to Consider:
What specific objects, people, or activities do you need to detect?
How will this detection trigger actions or workflows?
Examples:
Retail: Detect empty shelves to automate restocking alerts.
Security: Identify unauthorized individuals in restricted zones.
Facilities: Recognize spills to dispatch cleaning robots.
Having a clear objective ensures your model delivers actionable results.
Step 2: Collect and Tag Training Data
Object detection models require labeled examples to learn what to identify. LensPath simplifies this process with its user-friendly tagging interface.
How It Works:
Upload a collection of images or video snapshots that include the object(s) you want to detect.
Use LensPath’s tagging tool to outline and label each object in your dataset.
Include varied examples—different angles, lighting conditions, and environments—to improve model accuracy.
Tip: Start with at least 100-200 tagged examples for each object to achieve reliable detection performance.
Step 3: Train Your Model
Once your training data is ready, LensPath handles the rest.
How It Works:
LensPath processes the tagged data to train a custom object detection model.
The platform automatically optimizes the model for performance and size, ensuring compatibility with target devices like edge cameras or cloud processing systems.
Preview initial results and refine the model by adding more examples if needed.
Example: A retail store trains a model to detect low-stock conditions on shelves using images from their existing camera feeds.
Step 4: Deploy Your Model to Cameras
LensPath allows you to assign your custom model to specific cameras with just a few clicks.
Deployment Options:
Edge Processing: Models run directly on compatible cameras or edge devices (e.g., Cisco Meraki).
Cloud Processing: Models process visual data in the cloud, ideal for centralized workflows.
How It Works:
Select the cameras you want to assign the model to.
LensPath automatically configures the model to match the hardware requirements.
Start detecting objects in real time.
Step 5: Automate Workflows with No-Code Builder
Once your model is deployed, it’s time to create automated workflows based on detections.
Example Workflows:
Trigger: “Spill detected in a cafeteria.”
Action: Dispatch a cleaning robot to the location.
Trigger: “Empty shelf detected in aisle 4.”
Action: Notify store staff to restock products.
LensPath’s drag-and-drop no-code builder makes it easy to link detections to real-world actions.
Conclusion
Creating custom object detection models has never been simpler. With LensPath, businesses can train, deploy, and automate workflows without the need for technical expertise. Whether you’re in retail, security, or facilities management, LensPath empowers you to turn camera insights into real-world results.
Ready to build your first custom detection model? Contact us to get started and unlock the full potential of your camera network!
