The initial and essential phase in any deep learning application involves annotating training data. The caliber of these annotated datasets significantly influences the performance, precision, and resilience of the application.
Utilizing the Deep Learning Tool simplifies the data labeling process through its user-friendly interface, eliminating the need for programming expertise. This labeled data can be effortlessly incorporated into MVTec HALCON and MERLIC for executing deep learning tasks like object detection, classification, semantic and instance segmentation, anomaly detection, and Deep OCR. For classification and Global Context Anomaly Detection projects, the Deep Learning Tool offers the convenience of both model training and evaluation.
The latest update to the Deep Learning Tool introduces an enhanced smart labeling feature designed for segmentation tasks. This innovative tool offers automatic label suggestions upon hovering over specific areas within an image. Upon clicking, the suggested label becomes finalized.
Moreover, users now have the capability to directly train their semantic segmentation models within the Deep Learning Tool. Notably, enhancements have been made to the heatmap functionality, enabling a more comprehensive insight into individual decisions. Furthermore, the tool now supports the ability to resume training sessions after their initial completion.
