# Release Information

The VisionPro Deep Learning 4.0 version introduces the following features, enhancements, and changes:

## Features

- Introduced Few Sample mode for the Standard type Green Classify tool.
  This mode provides high performance with a small set of images, sometimes fewer than ten images per class.

  Note: Few Sample mode does not support the outlier score.

- Introduced Robust mode for the Standard type tools.
  This mode allows the tool to adapt to changes on the production line and to product variants that have similar kinds of defects. Select this mode if you want to use the tool on different production lines and new products without retraining the tool.

  Note: Robust mode replaces Adaptation mode in VisionPro Deep Learning. Adaptation mode is deprecated and removed from the application.

- Introduced outlier score for the Standard type Green Classify tool.

  The outlier score shows how much a view A view of an image is a region of pixels in an image. Tool processing is limited to the pixels within the view. You can manually specify a view, or you can use the results of an upstream tool to generate a view. deviates from the other views in the training Training is the process that your tool, which is a neural network, is learning about the features (pixels) based on the labels you made. For example, a tool will learn the defect/normal pixels in each image based on the defect/normal labels you drew. The goal of the tool Training is learning enough to give the correct inspection results of whether an unseen image is defective or not. The key to training is to ensure that you include all possible variations within your training set, and that your images are accurately labeled. Training times vary by the application, tool setup and the GPU in the PC being used to train the network. set. A high outlier score can indicate that an anomaly happened on the production like, for example, the lighting conditions changed.

  In the display filter, filter the score with the outlier_score syntax.

- Introduced the Accelerated Training as an expert mode parameter for Standard type tools.

  Accelerated training optimizes GPU resources and can contribute to speed improvements, but performance degradation can occur depending on the data.

- Introduced multiclass segmentation Segmentation is the process of selecting a view from an image. for the Standard and Legacy type Red Analyze tools. Multiclass segmentation is no longer a preview-mode feature A feature is a visually distinguishable area in an image. Features typically represent something of interest for the application (a defect, an object, a particular component of an object)..

- Introduced the Int8 option to Tensor RT speed optimization. The Int8 option improves processing time but it can reduce performance. You can also check the runtime processing time in Cognex Deep Learning Studio.

## Renewed Tool Categorization

VisionPro Deep Learning 4.0 changes the categorization of the tools. From this version, the tools have the categories of type and mode instead of architectures.

Standard type tools are not compatible with the tools created in previous versions of VisionPro Deep Learning. The following changes apply when upgrading to 4.0:

- High Detail architecture tools are discarded and set to Standard type Accurate mode.

- Focused architecture tools keep the same conditions as the existing tool. For example, a tool trained with Focused architecture and medium network model A specific spatial arrangement of a set of features (Blue Locate and Blue Read tools only.) During a post-processing step, the Blue Locate and Blue Read tools can fit all of the features detected in an image to the models defined for the tool. The overall pose and identity of the model is then returned. is set to Legacy type medium network model.

- If using a High Detail architecture tool in the runtime workspace A runtime workspace is a configuration file that does not contain images or databases, containing only streams and tools, which makes it a smaller version of a full Workspace. This configuration file can be loaded in the library in order to perform some analysis., you must retrain the tool in VisionPro Deep Learning 4.0.

The new tool types:

Tool | Type | Mode | Network Model | Corresponding 3.3 Network
Red Analyze | Standard | Fast | N/A | Red Analyze High Detail architecture, small network model
Accurate | Red Analyze High Detail architecture, normal network model
Robust | N/A
Unsupervised | N/A | Small | Red Analyze Unsupervised architecture, small network model
Medium | Red Analyze Unsupervised architecture, normal network model
Large | Red Analyze Unsupervised architecture, large network model
Legacy | N/A | Small | Red Analyze Supervised architecture, small network model
Medium | Red Analyze Supervised architecture, normal network model
Large | Red Analyze Supervised architecture, large network model
Green Classify | Standard | Fast | N/A | Green Classify High Detail architecture, small network model
Accurate | Green Classify High Detail architecture, normal network model
Robust | N/A
Few Sample | N/A
Legacy | N/A | Small | Green Classify Supervised, small network model
Medium | Green Classify Supervised, normal network model
Large | Green Classify Supervised, large network model

## Enhancements

- Improved the grid ROI The Region of Interest (ROI) defines the area of operation for the tool. The ROI preserves the positions, angles, stretch and skew of the original image. rule. The grid divides the ROI into several views. The following rules apply to all views that the grid creates:

  - Green Classify tool: The new views have the same class as the original view.

  - Red Analyze tool: The region remains in the same position as the label of the original view.

    - If a new view does not have a drawn region after you apply the grid, the view is labeled as Good.

    - If the original view did not have a label before applying the grid, the new views will not have a label either.

  - Blue Locate and Blue Read tools: The tools find the feature based on the new view.

- When adding a downstream tool after the Blue Locate tool, the default value for pixel is changed from 100x100 to fitted size from the Blue Locate tool when editing the ROI.

- The IOU The Intersection over Union (IOU) shows how much the predictions of the tool match your labeling. A higher IOU percentage indicates a better match. metric is added to the Region Area Metrics in the Database Overview.

- Introduced shortcuts to the drawing tools in the Edit ROI, Edit Mask, and Edit Region toolbars.

- Added the mismatch display filter to the Red Analyze tool.

## Changes

- In previous versions, sometimes the processing speeds shown in Cognex Deep Learning Studio and runtime environments were different. From this version, both environments show the same processing speed.

- Heatmap for the Standard type Green Classify tool is available in the runtime environment.

- The Speed Optimization parameter is added to the Tool Parameters panel under the Processing section. This parameter is applicable for Standard type tools, and it is the same as Tensor RT speed optimization in the runtime environment.

- Introduced an error message that appears if you do not have the minimum required number of views for training or label quality check

- The Threshold parameter of the Red Analyze tool changed. The tool now uses a single threshold instead of T1 and T2.

- The default option for the GPU Memory Optimization option changed to disabled.

- When using the Green Classify tool, you can tag with the number keys. If you press the 1 number key, you can apply the first tag in the tag list.

- When using multiple defect classes with the Red Analyze tool, you can change between classes in the Database Overview using shortcuts. The Database Overview displays the Score, ROC The Receiver Operating Characteristic (ROC) is a graphical tool that allows you to visualize and analyze the tradeoff between precision and recall exhibited by a particular set of training parameters when applied to a particular training image set., and Confusion Matrix only for the selected class. For example, pressing ALT + CTRL + 1 selects the first class in the list, and pressing ALT + CTRL + 0 selects the tenth class.

- You can check Untrained Views from the Region Area Metrics.

Note:

- Sample code is included with the VisionPro Deep Learning 4.0 installer. The resources for the sample code (VisionPro Deep Learning 4.0 Sample Code Resource.msi), which include images and workspaces, are available for download from the Cognex Support Site.
- For more information about new API functionality in this release, see the Cognex VisionPro Deep Learning Suite .NET Library and Cognex VisionPro Deep Learning Suite C API help files.