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 and MERLIC 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.
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
Working with the Deep Learning Tool
Labeling
for 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.
for 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.
for Segmentation: Polygons
Labeling for semantic segmentation and instance segmentation can be done by drawing polygonal regions around relevant objects.
for Segmentation: Pixel masks
Labeling for semantic segmentation and instance segmentation can also be done by painting pixel masks with brush and eraser that cover relevant objects.
for Segmentation: Smart Labeling
To greatly reduce labeling time and cost, a smart labeling tool can be used to benefit from label suggestions.
Training for classification
Users can set all important parameters and perform training based on their labeled data.
Progress of the training process
Evaluation for classification
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.
Evaluation of the trained networks
Seamless integration into the MVTec product portfolio
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.
The Deep Learning Tool is available for free download on our website.