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MVTec Deep Learning Tool

The Deep Learning Tool Offers:

  • A fast path to the complete Deep Learning solution
  • An intuitive user interface
  • Active support for the optimization of the trained networks
  • Easy integration into the MVTec portfolio
  • Full control over your own data

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For pricing and more details, get in touch with one of our friendly machine vision experts.

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Release Notes

 

Deep Learning Tool

The easy way into deep learning with MVTec software

Labeling training data is the first crucial step towards any deep learning application. The quality of this labeled data plays a major role when it comes to the application’s performance, accuracy, and robustness.

With the Deep Learning Tool, you can easily label your data thanks to the intuitive user interface – without any programming knowledge. This data can be seamlessly integrated into HALCON to perform deep-learning-based object detection, classification and semantic segmentation. For classification projects, you can also train and evaluate your model in the Deep Learning Tool.

Working with the Deep Learning Tool

Labelling

Object Detection

With object detection, labeling is done by drawing rectangles around each relevant object and assigning these rectangles to the corresponding classes. Depending on the project requirements, the user can label his data with either axis-parallel or oriented rectangles.

 

 

 

 

 

 


Classification

Labeling for classification is done by simply importing the images and assigning them to a class. If the images are stored in appropriately named folders, they can also be labeled automatically during import.

 

 

 

 

 

 

 

 


Semantic Segmentation

Labeling for semantic segmentation is done by drawing polygonal regions around relevant objects.

 

 

 

 

 

 

 

 

Training for Classification

Progress of the training process

Users can set all important parameters and perform training based on their labeled data.

 

 

 

 

 

 

 

 

 

Evaluation for Classification

Evaluation of the trained networks

Users can evaluate and compare their trained networks directly in the tool. The evaluation section provides information on model accuracy, including a heatmap for the predicted classes of all processed images, as well as an interactive confusion matrix to help detect misclassifications. Users can also calculate the estimated inference time per image and export the evaluation results as a single HTML page for documentation purposes.

 

 

 

 

 

 

 

 

 

Seamless Integration into the MVTec Product Family

The Deep Learning Tool seamlessly integrates into the MVTec product portfolio with HALCON and MERLIC and serves as the core of your Deep Learning application.

Acquire your images and preprocess them with HALCON or MERLIC if necessary. After labeling, training as well as evaluation in the Deep Learning Tool, deploy your trained network in the respective runtime environment.