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MVTEC Deep Learning Tool 24.05 Full Version x64 windows 深度学习工具dlt-24.05完整版

文件名: dlt-24.05.zip
文件大小: 3967849578 字节 (3.70 GB)
修改日期: 2024-05-24 10:19
MD5: 7491f92d86832de8429275ad4b34c0d0
SHA1: 41488f22b38f460a090db1977e23847259b5a676
SHA256: 687beee9ea176d1cbb54744367fc46ac27be450e290d5220234d0336cb9839e6
CRC32: 8a36ac83

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MVTEC Deep Learning Tool 24.05 Full Version x64 windows 深度学习工具dlt-24.05完整版

http://visionbbs.com/thread-32233-1-1.html

(出处: 石鑫华视觉论坛)

Version 24.05
New Features

• The Deep Learning Tool now allows optimizing trained models for Hailo devices. For this, a WSL image is provided, which has to be installed separately. This enables a special Hailo device emulator on the Evaluation and on the Export page. For the optimization step, no real Hailo device is required.

• It is now possible to circumvent the expiry date of the Deep Learning Tool by providing a valid and suitable HALCON license file for the HALCON version used by the DLT. This license file must be passed to the DLT via the command line option --use-rtl <license file>.
  If there is a problem with the license of the DLT, the explanation in the license error dialog is now more meaningful.
  The About dialog displays more information about the used HALCON version, as well as the CUDA version used by HALCON. In addition, this information is now also printed into the log file.

• The functionality to detect the maximum batch size has been removed.

• The DLT now offers a light theme for the UI. Light and dark theme can be toggled in the Preferences dialog.

• For GC-AD projects, it is now possible to select a trained subnetwork for export before an evaluation has been done.

• On the Export page, there is now a checkbox to control whether the generated evaluation report should be opened after generation in the browser.

• The “Duplicate Training” option now duplicates not only the trained model but also the results and is now also available on the Evaluation page.

• For the int8 optimization in Anomaly Detection projects, the Deep Learning Tool now only uses “good” images for quantization.

• It is now possible to open a training folder in the Windows Explorer via the context menu in the training list.

• The optimization of a deep learning model is now an explicit step in the DLT. There is a button to start the optimization and a progress bar that shows the optimization progress.

• The icon for sorting the images on the Evaluation page has been made more comprehensible.

• The Release Notes page of the documentation now mentions which HALCON version is used and the expiration date of the license for the current and previous DLT versions.

• The DLT is now based on HALCON 23.11 Progress. This includes an update of the underlying CUDA library to version 12.1.

• The class distribution table on the Split page can now be clicked to zoom in on a selected class. This can help if the distribution of classes is unbalanced.

• The descriptions of the example projects now also mention that the number of provided images is reduced to save space and that satisfactory training results should not be expected.

• Microsoft Windows Server 2022 has been added as a supported operating system.

• It is now possible to filter images by the number of channels, as well as the image width and height.
  

Resolved Issues and Improvements

• If an error occurred during an evaluation, for that training the misleading status "Training failed" was displayed. This problem has been fixed.

• Loading a DLT project was denied if a single training or evaluation file was corrupted. This problem has been fixed. Now, the project is loaded, the corrupted file is ignored, and the log file contains an error message.

• Saving a project under a name that already existed merged the current project with the existing one. This problem has been fixed. Now, "Save as" will overwrite all trainings when overwriting an existing project.

• While an evaluation was running, it was not possible to show the quick help on the Evaluation page. This problem has been fixed.

• On the Evaluation page, the shown IoU values could be inconsistent due to rounding errors. This problem has been fixed.

• The area of the background class on the Split page could be wrong for very high area values. This problem has been fixed.

• Importing an Anomaly Detection project could lead to a wrong split distribution. This problem has been fixed.

• An imported dataset could have the wrong split distribution. This problem has been fixed.

• The start-up time has been improved in some cases.

• The "Split mask regions" button on the Image page for Semantic Segmentation projects was enabled even when no region was selected. This problem has been fixed.

• Exporting a model with int8 precision led to an error. This problem has been fixed.

• For Semantic Segmentation, when trying to export a model for an OpenVINO AI² interface, invalid values for the precision could be selected. This problem has been fixed.

• The DLT could crash if image files in a project were moved on the disk before starting the evaluation. This problem has been fixed.

• After opening an empty project, it was sometimes possible that the selected page in the tool was disabled. This problem has been fixed.

• The DLT could crash if the %TEMP% folder did not have enough free space. This problem has been fixed.

• The list of trainings was sorted differently on the Training and on the Evaluation page. This problem has been fixed.

• The parameter "anchor_angles" was missing in the exported report for trainings that used this parameter. This problem has been fixed.

• For Semantic Segmentation, the initialization of the class weights was wrong after resuming a training. This problem has been fixed.

• The set path was not used for labeling when exporting datasets for Semantic Segmentation. This problem has been fixed.

• During a training with many iterations, the DLT could freeze after around 20,000 to 40,000 iterations and become inoperable. This problem has been fixed.

• Switching the preprocessed image view on and off in full image mode on the Evaluation page continuously decreased the image size for non-quadratic images. This problem has been fixed.

• In the Preprocessed Image Preview dialog on the Training page, the patch size indicator did not stay at the new position after moving. This problem has been fixed.

• For Anomaly Detection projects, the default value of the first epoch after which the learning rate is reduced is now 95% of the total number of epochs.

• When the DLT was configured to open the latest project at program start, the class distribution was not shown correctly when re-opening the Split page. This problem has been fixed.

• Label regions were sometimes not shown properly on the Review page for certain combinations of margin, size, and aspect ratio. This problem has been fixed.

• The performance of removing images, especially in projects with more than 10,000 images, has been improved noticeably.

• When drawing a label mask with the brush, some systems showed significant lag between the movement of the mouse and the movement of the drawing brush cursor. This problem has been fixed. Now, the brush cursor follows the mouse movement immediately.

• When drawing polygons, pressing the Shift key did not work as expected. This problem has been fixed.

• Resetting a training neither reset the number of epochs nor the number of iterations of the training. This problem has been fixed.