MVTec Deep Learning Tool

Deep Learning Simplified

MVTec Deep Learning Tool provides developers with a fast, intuitive path to high-quality deep learning applications. Integrated with HALCON and MERLIC, the tool supports a range of powerful features:

• Object Detection

• Segmentation

• Anomaly Detection

• Deep OCR

With a user-friendly interface and seamless integration, it’s the complete solution for labelling, training, evaluating, and deploying deep learning models.

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Choose Your Edition

Free Edition

Ideal for experimentation and small-scale projects. Includes:

• Core labelling, training, and evaluation tools

• Easy-to-use interface

• Automatic labelling on import

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Professional Edition

Designed for advanced development and integration. Includes:

• Enhanced dataset management

• Extended model configuration

• Long-term support with HALCON & MERLIC

[Learn more about the Deep Learning tool Professional Edition]

OEM Edition

Perfect for high-volume deployments and product embedding. Includes:

• Deep customisation and integration options

• Licensing suited for OEMs

[Contact Us About OEM Licensing]

Labelling

Data labelling is an essential task in many deep learning projects, providing the system with essential information about how to solve the problem correctly. This information may include image classes, object locations, or pixel masks assigned to classes or instances.

Labelling for Classification
• Import images and assign them to classes. If images are organised in correctly named folders, they can be labelled automatically during import.

Labelling for Object Detection
• Draw rectangles around each relevant object and assign these to corresponding classes. Label with either axis-parallel or oriented rectangles depending on project needs.

Labelling for Segmentation
• Create polygonal regions or paint pixel masks using brush and eraser tools to cover objects for semantic and instance segmentation. Smart labelling tools offer instant suggestions, speeding up the process.

Labelling for Deep OCR Training
• Retrain Deep OCR models for improved recognition in specialised applications. The tool offers workflows that automatically generate label proposals, which can be accepted or refined easily.

Labelling for Global Context Anomaly Detection
• Simply import images and assign them to "good" or "anomaly" classes. Automatic labelling during import is available when images are properly organised.

Training: Controlled, Transparent and Flexible

During training, a pretrained classifier learns from the labelled dataset by iterating over the data to improve predictions based on validation results. The neural network weights are adjusted to enhance performance progressively.
• Users configure key parameters on the training page, select data splits, and monitor training progress with real-time visualisation of performance metrics.

Training currently supports the following deep learning methods:
• Classification
• Global Context Anomaly Detection
• Object Detection
• Instance Segmentation
• Semantic Segmentation
• Deep OCR (Detection and Recognition)

Evaluation: Comprehensive and Insightful

Evaluation tests the trained model against a test dataset to predict real-world performance. It provides machine vision specialists with critical insights into model accuracy and reliability.
• Users can evaluate and compare trained networks directly within the tool.
• Features include heatmaps for predicted classes, an interactive confusion matrix to identify misclassifications, and estimation of inference time per image.
• Evaluation results can be exported as an HTML report for documentation.

Supported evaluation methods include:
• Classification
• Global Context Anomaly Detection
• Object Detection
• Instance Segmentation
• Semantic Segmentation