Deep Learning Tool
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 MVTec Deep Learning Tool, we are creating a comprehensively smooth deep learning experience for HALCON users. By developing our own solution, we make sure that its output can seamlessly be integrated into HALCON. This application also allows us to incorporate our extensive experience and expertise that we acquired while developing HALCON's deep learning algorithms which – naturally – involved a lot of labeling, too.
Working with the MVTec Deep Learning Tool
The current version of the Deep Learning Tool (0.2) provides labeling functionality for HALCON's deep-learning-based object detection. This labeling is done by drawing rectangles around each relevant object and by providing information about their corresponding classes. Depending on the project's requirements, users can choose between labeling their data with axis-aligned rectangles, or with oriented bounding boxes.
To speed up the labeling process, multiple users can work on different parts of the image data set. E.g., a user can fully label a subset of images or label just a single class on all images within a data set. These partial data sets can then be merged into one project via different HALCON dictionaries.
Labeling functionality for HALCON's deep-learning-based classification and semantic segmentation will be added in the next versions of the MVTec Deep Learning Tool. To help you label data for these deep learning technologies, we provide Deep Learning Helpers for download.
The Deep Learning Tool is available in English, German, Japanese and Chinese (Simplified) language.