MVTEC Deep Learning Tool 24.05.1 Full Version x64 windows 深度学习工具dlt-24.05.1完整版

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MVTEC Deep Learning Tool 24.05.1 Full Version x64 windows 深度学习工具dlt-24.05.1完整版

文件名: dlt-24.05.1.zip
文件大小: 3979706263 字节 (3.71 GB)
修改日期: 2024-07-18 16:28
MD5: 1260102b069c9e336fd4268610c9e573
SHA1: b619ee2918d40efeded93a49c09da99dcea15763
SHA256: c8e0898c52363cd2483b165e86766d29cd3a595bddd21221b2b68ffd9dd55c32
CRC32: efd0e776

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Version 24.05.1
New Features

• To improve training in very rare cases for Anomaly Detection projects, the option to set the advanced training parameter 'Weight Prior' has been added.
• It could happen that the DLT could not be started if another application was installed that used an incompatible version of OpenVINO. This problem has been fixed. Now, the OpenVINO version used by the DLT no longer interferes with other OpenVINO installations.
  

Resolved Issues and Improvements

• In the Chinese translation of the documentation, an entry in the “Minimum System Requirements” table was missing. This problem has been fixed.
• The status of the Heatmap button on the Evaluation page could be misleading if a project was opened while the list of available DL devices was not ready. This problem has been fixed.
• After optimizing a model for TensorRT or OpenVINO, then switching to Hailo and changing the setting for the option 'Use allocation script', the prior optimizations seemed to be lost. This problem has been fixed.
• In GC-AD projects, when drawing a polygon to restrict the domain for postprocessing, the lines were invisible during the drawing. This problem has been fixed.
• When optimizing a model for Hailo, there is now an option that allows enabling a fine-tuning step during optimization. This can improve the performance of the optimized model with the trade-off of a longer optimization time.
• In some cases, the placeholder text in the comment area was not located at the right position. This problem has been fixed.
•     If in a GC-AD project from a combined trained model only the local or global subnetwork was selected for optimization, this selection was ignored, and always the combined network was optimized. This problem has been fixed.
• When duplicating a training during a running optimization, the wrong optimization state was shown in the duplicated training. This problem has been fixed.
• If an error occurred during the inference calculation, e.g., due to a missing image, it could happen that the error message was not announced properly. In some rare cases, the DLT could even crash. These problems have been fixed.
• Several clicks on the Duplicate entry in the training item context menu could create several duplicates of the training. This problem has been fixed.
• When selecting images or items on the Evaluation page, unintuitive effects could occur. This problem has been fixed.
• In Semantic Segmentation projects, the optimization of trained models for OpenVINO with precision fp16 failed in most cases. Hence, this option was removed from the precision selection box. Further, in some cases, the precision selection box contained unsupported or not all supported values. These problems have been fixed.
• If the learning rate was changed in the text box and afterward, another training parameter (e.g., number of iterations) was changed using the spin box or mouse wheel, the learning rate changed back to its old value. This problem has been fixed.
• The documentation now provides a more detailed description of anomaly score and anomaly score tolerance.