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 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.
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.