MVTEC Deep Learning Tool 25.04.2 Full Version x64 windows 深度学习工具dlt-25.04.02完整版

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MVTEC Deep Learning Tool 25.04.02 Full Version x64 windows 深度学习工具dlt-25.04.2完整版

文件名: dlt-25.04.2.zip
文件大小: 4426309811 字节 (4.12 GB)
修改日期: 2025-09-26 22:25
MD5: f44fb61a6fbcf152286bee9ac1919e1c
SHA1: bad970adc972778c6ce91e734b83c67ef3e66b6c
SHA256: 073fee5ad30f9cdf163f80e7234f40f0797f50cd9afdcca38bceaa59605371c0
CRC32: c5f80aa6

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发行说明：
Version 25.04.2
New Features新功能

• 
  It is now possible to use the Deep Learning Tool Professional or the Deep Learning Tool OEM with a MERLIC dongle if an appropriate DLT Pro or DLT OEM license for the dongle is available, respectively.现在，若拥有适用于该加密狗的相应DLT Pro或DLT OEM许可证，即可使用配备MERLIC加密狗的深度学习工具专业版或深度学习工具OEM版。
  

Resolved Issues and Improvements已解决的问题与改进

• 
  The default value of the evaluation parameter “evaluation_interval_epochs” was not set. This could lead to false behavior if the parameter was missing in the model. This problem has been fixed.评估参数“evaluation_interval_epochs”的默认值未被设置。若模型中缺少该参数，可能导致错误行为。此问题现已修复。
• 
  In Instance Segmentation projects, the setting “Instance Segmentation: Only bounding box” was not evaluated correctly, resulting in false evaluations. This problem has been fixed.在实例分割项目中，“实例分割：仅边界框”设置未能正确评估，导致评估结果出现错误。该问题现已修复。
• 
  In some cases, the DLT could not load large images. This problem has been fixed.在某些情况下，DLT无法加载大型图像。该问题现已修复。
• 
  If no suitable device was available, there was a problem with the selectable device for training. This problem has been fixed.如果没有合适的设备可用，说明用于训练的可选设备存在问题。该问题现已修复。
• 
  For Hailo devices, the option to use an alloc script and the option to finetune were not displayed on the Evaluation page. Further, a displayed warning message was not reset under some circumstances when changing the DL device. These problems have been fixed.对于Hailo设备，评估页面未显示使用分配脚本和微调的选项。此外，在某些情况下更换DL设备时，显示的警告信息不会重置。这些问题现已修复。
• 
  There was an issue leading to an unreadable configuration file in the example directory. This problem has been fixed.示例目录中存在导致配置文件无法读取的问题，该问题现已修复。
• 
  The selected split for the training was not considered on the Evaluation page. This problem has been fixed.训练中选定的分割方案未在评估页面中考虑。此问题现已修复。
• 
  The documentation missed explaining the “Limit samples per class” parameter for Out-of-Distribution detection. This problem has been fixed.文档遗漏了对分布外检测中“每类样本限制”参数的说明。该问题现已修复。
• 
  When fitting for out-of-distribution detection, the state was falsely displayed for all trainings. This problem has been fixed.在进行分布外检测的拟合时，所有训练数据的状态均被错误显示。该问题现已修复。
• 
  The command line option --use-rtl has been removed.命令行选项 --use-rtl 已被移除。