Release Notes
Mech-DLK 2.6.0 Release Notes
This section introduces the new features and improvements of Mech-DLK 2.6.0.
New Features
Added the Parameter Settings Feature for the Smart Labeling Suite
Mech-DLK provides the smart labeling suite for you to efficiently label datasets.
The smart labeling suite includes the following labeling tools:
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Smart Labeling Tool
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Pre-trained Labeling Tool (formerly “Pre-labeling Tool”)
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Visual Foundation Model (VFM) Labeling Tool (formerly “Super-labeling Tool”)
Mech-DLK 2.6.0 has added the feature that allows you to configure parameters for the smart labeling suite. When you use the smart labeling suite, you can configure the following parameters:
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In the menu bar, click , and enable Always load smart labeling model. This reduces the frequency of model unloading in open projects, minimizing the number of times you need to wait for the labeling model to reload.
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In the Instance Segmentation and Object Detection modules, when you are using the smart labeling suite, you can click the Settings button in the upper left corner of the selection region to configure labeling parameters. For more information about parameter description, see Introduction to Instance Segmentation Labeling Tools and Introduction to Object Detection Labeling Tools.
Improvements
Optimized the Training Parameters for Defect Segmentation
Mech-DLK 2.6.0 has optimized the training parameters for the Defect Segmentation module. You can now train a High-accuracy or High-speed model.
Optimized the Algorithm for Fast Positioning
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Optimized training process: The Set Template button and Quick Template Tool are removed. The image adjustment operation is now performed during model validation, and it affects only the validation results. For more information, see Use the Fast Positioning Module.
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New labeling tools: Polygon Tool, Ellipse Tool, Rectangle Tool, Smart Labeling Tool, Mask Polygon Tool, Mask Brush Tool, Mask Lasso Tool, Mask Eraser Tool, and ROI Tool.
Optimized the Algorithm for Unsupervised Segmentation
Compared to the original algorithm, the optimized Unsupervised Segmentation algorithm increases training speed by 7 times, increases GPU inference speed by 2 times, and increases CPU inference speed by 10 times.
Supported VFM Labeling Tool for Text Detection
Mech-DLK 2.6.0 has supported the use of the VFM Labeling Tool in the Text Detection module. You can use this tool to label text areas in batches.
Optimized the Export Options for Cascaded Models
In Mech-DLK 2.6.0, you can export a single model or all models from the cascaded models.
Optimized the Add Module Window
Mech-DLK 2.6.0 has optimized the Add Module window, dividing the algorithm modules into Categorization, Locating, Inspection, and OCR algorithms. Additionally, functional descriptions for each module have been added to help you select the module that best fits your business needs.
Mech-DLK 2.5.3 Release Notes
This section introduces the new features and improvements of Mech-DLK 2.5.3.
New Features
Added the Super-labeling Feature
You can import new images to the current module and use the Super-labeling Tool to label the images in batches. You can fine-tune the results after labeling is completed.
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Supported modules: Instance Segmentation and Object Detection.
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Prerequisites: The available GPU memory on the current device must be greater than 6 GB; otherwise, the model conversion may fail.
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The Super-labeling Tool can be used only on the following three types of data:
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Unlabeled data
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Automatically labeled data (images with a yellow triangle at the front of the image number)
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Manually fine-tuned data after automatic labeling (images with a yellow triangle at the front of the image number)
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Three methods to use the Super-labeling Tool:
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The Super-labeling Tool in the labeling toolbar
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The Super-label button in the upper part of the image list
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The Super-label option in the right-click menu
For more information, see Super-labeling.
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Improvements
Supported the Mask Tools
Mech-DLK 2.5.3 supported the use of Mask Polygon Tool, Mask Brush Tool, Mask Lasso Tool, and Mask Eraser Tool in some algorithm modules.
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Modules that now support mask tools: Unsupervised Segmentation, Instance Segmentation, and Object Detection.
Supported Configuring the Mask Type
Mech-DLK 2.5.3 supported configuring the mask type as “Mask globally” or “Mask single image” in some algorithm modules.
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Modules that now support mask type configuration: Instance Segmentation and Object Detection.
Optimized the ROI Settings
Mech-DLK 2.5.3 optimized the settings for the ROI tool. After you adjust the ROI, click the OK button in the lower right corner of the ROI to save it.
Optimized the Threshold for Unsupervised Segmentation
Mech-DLK 2.5.3 optimized the default threshold position displayed in the Unsupervised Segmentation module. When the OK and NG curves do not intersect, the default threshold is set to the average of the OK and NG thresholds.
Deleted Some Parameters in Defect Segmentation
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The "Training Parameter Settings>Training parameters>Model type>Enhanced" parameter is deleted from the Defect Segmentation module in Mech-DLK 2.5.3.
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In Developer Mode, the "Training Parameter Settings>Data augmentation>Dilation" parameter is deleted from the Defect Segmentation module in Mech-DLK 2.5.3.
Mech-DLK 2.5.2 Release Notes
This section introduces the new features, improvements, and resolved issues of Mech-DLK 2.5.2.
New Features
Added the Pre-labeling Feature
After you validate a model, you can import new image data to the current module and use the pre-labeling feature to perform auto-labeling based on this model. You can fine-tune the results after labeling is completed.
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Supported modules: Instance Segmentation, Object Detection, Classification, Defect Segmentation, and Text Detection.
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Prerequisites: The pre-labeling feature is available only when the current module contains validated models.
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The pre-labeling feature can be performed only on the following three types of data:
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Unlabeled data
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Automatically labeled data (images with a yellow triangle at the front of the image number)
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Manually fine-tuned data after automatic labeling (images with a yellow triangle at the front of the image number)
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Three methods to perform the pre-labeling operation:
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the Pre-labeling Tool in the labeling toolbar
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the Pre-label button in the upper part of the image list
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the Pre-label option in the right-click menu
For more information, see Pre-labeling.
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Added the Image Tag Drop Down List
The Image tag drop down list is now available when you use cascaded modules and import data from the previous module. You can select the tag that you need to import corresponding images. For more information, see Use Cascaded Modules.
Improvements
Supported the Import of Datasets in the COCO Format
Some modules in Mech-DLK 2.5.2 allow you to import datasets in the COCO format.
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Supported modules: instance segmentation and object detection.
Integrated the Data Import and Export Features
In Mech-DLK 2.5.2, the Import button in the upper-left corner of the user interface is replaced by an Import/Export button, which integrated the data import and export features.
You can perform the import and export operations more easily.
Optimized the Data Export Settings
Mech-DLK 2.5.2 optimized the data export settings (also applicable to cascaded modules):
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Export all data from the current module: In the upper-left corner of the user interface, click the Import/Export button, then select Export Dataset.
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Export part of the data from the current module: In the image list, select one or more images, then right-click and select Export Dataset.
Resolved Issues
The following issues have been resolved in Mech-DLK 2.5.2:
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Fixed the issue that the verification results were also displayed in single image masks or global masks configured in the Defect Segmentation module.
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Fixed the issue that global masks configured in the Defect Segmentation module did not scale proportionally to the size of images.
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Fixed the software crash caused by unusual operations when using the Rectangle Tool.
Mech-DLK 2.5.1 Release Notes
Mech-DLK 2.5.1 improved the Smart Labeling Tool to enhance the labeling efficiency.
Mech-DLK 2.5.0 Release Notes
This section introduces the new features and improvements of Mech-DLK 2.5.0.
New Features
Added Text Detection and Text Recognition Modules
Mech-DLK 2.5.0 now features optical character recognition (OCR) that is realized by cascading the following two algorithm modules:
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Text Detection: the first module, which is used to detect a single line or multiple lines of text in an image.
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Text Recognition: the second module, which is used to recognize the characters in the text area.
This feature is usually employed to detect and recognize tiny characters on electronic components, vehicle-related data against complex backgrounds, and serial numbers and lot numbers displayed on the packaging.
Added the Unsupervised Segmentation Module
Mech-DLK 2.5.0 now features the Unsupervised Segmentation module, which only requires OK images in the training set to achieve pixel-level detection of all known and unknown defects. The trained model can be used to judge whether the image of an object is OK, NG, or Unknown on the basis of set thresholds. Moreover, heat maps are available to show the possible areas with defects.
This module is usually used for industrial quality inspection in scenarios where
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obtaining OK images is easy while getting NG images is difficult, and
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defect types are uncertain.
Added the Free Rectangle Tool
In the Text Detection module, the Free Rectangle Tool can be used to draw a rectangle around the text area. It is recommended to select this tool for rectangular text areas.
Added the Text Recognition Tool
In the Text Recognition module, the Text Recognition Tool can be used to define the text recognition range and automatically generate the recognition result.
Added OK/NG Labels
Mech-DLK 2.5.0 now allows to directly label images as OK or NG with the OK/NG Labels. Select an image from the image list and click the OK Label or NG Label on the toolbar. The image will be labeled as OK or NG.
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Both OK Label and NG Label are available in the Unsupervised Segmentation module.
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OK Label is available in the Defect Segmentation module.
Release Notes of Previous Versions
Click here to view Mech-DLK 2.4.x release notes
Mech-DLK 2.4.2 Release Notes
Added region-specific license control. Click to view the details.
Mech-DLK 2.4.1 Release Notes
New Features
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Added the Cascade Mode
Mech-DLK 2.4.1 has added a brand-new cascade mode of modules, which enables the combination of modules to solve deep learning problems in complex scenarios. It should be noted that when the Fast Positioning module is involved, it must be the first module. For example, when you need to detect the positions of defects and classify these defects, you can first add a Defect Segmentation module and then add a Classification module. In addition, when importing data from the previous module, you can select images according to actual needs and configure the import of these images in the Import window.
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Added the Training Center
The Training Center supports the training of models in a queue, which is suitable for scenarios requiring the training of multiple models. With the Training Center, the software can train models in sequence, with no need for manual clicking of Train repeatedly, which can save a huge amount of time.
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Added the Mask Type of Mask Globally and Supported Custom Mask Fill
In the Defect Segmentation module, when you select the Mask Polygon Tool, the mask type can be Mask single image or Mask globally. In addition, you can customize the mask color.
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Mask single image: The mask is only displayed in the current image. It is only valid in training.
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Mask globally: After a mask is drawn in the current image, the mask will be displayed in all images. The mask is valid in both training and validation.
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Added the Floating Window of Keyboard Shortcuts
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Added Auxiliary Labeling Lines for Rectangle Tool
In the Instance Segmentation and Object Detection modules, auxiliary labeling lines are added for the Rectangle Tool to assist rectangular selection on images.
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Added the Display and Filtering of Validation Result Confidence
In the Instance Segmentation and Object Detection modules, a confidence filtering function is added for validation results. You can adjust the confidence to filter the validation results and then evaluate the accuracy of models.
Improvements
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Optimized the Classification Module
The Classification module is optimized, which leads to faster training convergence and a growth rate of 20% in accuracy under complex scenarios.
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Optimized Mech-DLK SDK
Mech-DLK SDK is more stable and easier to use after being restructured. Mech-DLK SDK supports the inference based on the cascaded modules and the switching between different operating hardware. In addition, it provides richer samples for reference.
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Optimized the Setting of Defect Determination Rule
The defect determination rule in the Defect Segmentation module is optimized. Click here to view the details.
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Enabled the Setting of Translation in the Fast Positioning Module
In the Image Adjustment window of the Fast Positioning module, you can translate the image along the X and Y axes. After training, images with objects in specified positions and orientations will be generated, which meet the requirements of more application scenarios.
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Optimized the Template Tool
In the Instance Segmentation and Object Detection modules, after selecting the Template Tool, press and hold the Shift key and scroll the mouse wheel to adjust the angle of the template. You can also set the Rotation angle to achieve the same purpose.
Click here to view the Mech-DLK 2.3.0 release notes
Mech-DLK 2.3.0 Release Notes
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Graphics Card Driver Requirement
Before using Mech-DLK 2.3.0, please upgrade the graphics card driver to 472.50 or above.
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Improved the Training Speed
Optimized the algorithms, and thus significantly improved the speed of model training. Only the optimal model is saved during training, and the training cannot be stopped halfway.
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Added the Smart Labeling Tool
For modules including Defect Segmentation, Instance Segmentation, and Object Detection, you can do smart labeling by selecting the Smart Labeling Tool, clicking the objects to be labeled, right-clicking to undo the redundant selection, and pressing the Enter key to complete the labeling.
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Added the Function of Adding/Removing Vertices for the Polygon Tool
For the Instance Segmentation and Object Detection modules, after labeling with the Polygon Tool, if the selection needs to be modified, you can left-click the line segment between two vertices to add a vertex, or right-click a vertex to remove it.
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Added the Template Tool
For the Instance Segmentation and Object Detection modules, you can use the Template Tool to set the selection as a template. The template can be applied by simply clicking the images. It is suitable for scenarios where there are multiple neatly arranged objects of the same type in an image, and it improves labeling efficiency.
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Added the Function of Preview by Zooming
Support previewing full images and cropped cell images.
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Optimized the Grid Cutting Tool
Optimized the Grid Cutting Tool. After cutting the image by the grid, you can select a cell image by checking the box in the upper left corner of the cell image, and you can preview the image by clicking on the button in the upper right corner of the cell.
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Optimized the Data Filtering Mechanism
Added options for filtering results: “Correct results”, “Wrong results”, “False negative”, and “False positive”. Added options for filtering data types: “Labeled as OK” and “Labeled as NG”.
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Built-in Deep Learning Environment
The deep learning environment is built into the software Mech-DLK, and the models can be trained without a separately installed environment.
Click here to view Mech-DLK 2.2.1 release notes
Mech-DLK 2.2.1 Release Notes
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Added the Function of Showing the Class Activation Maps for Module Classification
After the model is trained, click Generate CAM. The class activation maps show the weights of the features in the form of heat maps; the model classifies an image into its class according to these features. Image regions with warmer colors have higher weights for classifying the image into its class.
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Supported Validation and Export of CPU Models
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Classification and Object Detection: After training is completed, select the deployment device as CPU or GPU before exporting the model.
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Instance Segmentation: Before training the model, set the training parameters. When exporting a model, select the deployment device as CPU/GPU:
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CPU lightweight model: Before training the model, set the training parameter Model type to Lite (better with CPU deployment). When exporting the model for deployment, set Deployment device to CPU or GPU.
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GPU standard model: Before training the model, set the training parameter Model type to Normal (better with GPU deployment). When exporting the model for deployment, set Deployment device to GPU.
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