# Release Notes

As of VisionPro Deep Learning 3.0, Cognex brings a couple of brand new features that steer your machine vision journey to the new round of excellence. For the updates made in API, see API.

## Adaptation Mode for High Detail tools

Sometimes, the newly captured images in the front lines of your daily operation can deviate from the training images that constructed your VisionPro Deep Learning tool which handles the incoming images, due to many reasons. For example, the height of your cameras in your factory line can change as the camera mounting plates or rods are worn out, and this can lead to unexpected variations in incoming images compared to the initial images. In this case, the result of a VisionPro Deep Learning tool also starts to deviate against the expected result because the tool was trained on initial images, not on the new images that contain variations. Adaptation Mode was invented to handle this kind of problem by filling the performance gap between the result from training images and the result from newly captured images by adapting a previously trained tool to the new images with variations. Adaptation Mode is supported only for Red Analyze High Detail and Green Classify High Detail tool. See Adaptation Mode: Adapting to Line Variation for details.

## Red Analyze High Detail and Green Classify High Detail Architecture Upgrade

The neural network architecture of Red Analyze High Detail and Green Classify High Detail is updated in VisionPro Deep Learning 3.0.

- Red Analyze High Detail is evolved to provide higher accuracy. In many cases, the new architecture of Red Analyze High Detail shows higher accuracy than the old architecture.

- Green Classify High Detail is evolved to provide higher accuracy. Especially, the focus of Green Classify High Detail update is on the Large network model among Green Classify High Detail Network Model parameters (Small, Normal, Large), and it aims to provide the followings.

  - Higher Accuracy

  - Removal of Extra Large in Network Model parameters

### Sampling without "Stride"

The sampling logic for Red Analyze High Detail training evolved that it can deliver the same performance without the Stride-based sampling. Due to this upgrade, the Stride in the Training parameters of Red Analyze High Detail is deprecated from VisionPro Deep Learning 3.0.

### Backwards Compatibility

Due to this upgrade, you need to consider the followings to use a Red Analyze High Detail or a Green Classify High Detail tool trained in the earlier VisionPro Deep Learning versions. For more details, see Migrating to This Release.

- Opening a workspace and a High Detail tool trained in an earlier VisionPro Deep Learning version on VisionPro Deep Learning 3.0

  - The trained network of this tool will be lost.

  - The views and their labels of this tool will be preserved.

  - The Tool Parameter values will be preserved.

- Importing a Red Analyze High Detail tool (.vrta) or a Green Classify High Detail tool (.vgta) trained in an earlier VisionPro Deep Learning version into a VisionPro Deep Learning 3.0 workspace

  - The trained network of this tool will be lost.

  - The views and their labels of this tool will be preserved.

  - The Tool Parameter values will be preserved.

### Change of Validation Loss Calculation

Another change from this architecture update is the new validation loss calculation for Red Analyze High Detail, which is used to select the best neural network during validation. See Validation Set and Validation Loss for more details.

## Automatic Parameter Search

A new mode for Parameter Search is introduced for a fast automatic search. Given a search range, the automatic mode (auto mode) automatically prunes the values of parameters to get you the best possible results within the shortest time. The existing parameter search is now called the manual mode. While the manual mode tests all combinations of the listed parameters and their value ranges, the auto mode cuts off unnecessary tests based on performance criteria and thus it can save more time for getting you the best values of provided parameters. The auto mode of Parameter Search is only supported for High Detail tools. See Parameter Search for details.

## Label Check

If you deal with a large amount of image data, it is difficult to label all the data and control the labeling tasks. In this case, you are vulnerable to wrong or noisy labels and it takes so much cost to find wrong or noisy labels. For such a case, Cognex offers the Label Check feature to automatically review the goodness of the labels in your images. By simply clicking a few buttons, the label quality information is displayed for each view. The label quality value is scaled from 0 to 100. The lower the value, the noisier or the worse each label is. You can dramatically reduce the time spent reviewing the labels by sorting images in ascending order by the label quality value. Label Check is only supported for Red Analyze Focused Supervised, Red Analyze High Detail, and all Green Classify tool types. See Optimize Labeling with Label Check for more details.

## Cognex Label Import/Export Plugin (CLIP)

CLIP is a new plug-in that supports importing/exporting masks and labels of a VisionPro Deep Learning workspace in VisionPro Deep Learning internal and external formats. You can export masks, labels, or images in a VisionPro Deep Learning workspace in VisionPro Deep Learning internal format or png file format, and also import masks or labels in various formats into a VisionPro Deep Learning workspace with this plug-in. See Cognex Label Import/Export Plug-in for details.

## TensorRT Support for Runtime

The NVIDIA TensorRT is a library that boosts the inference speed of a deep neural network running on NVIDIA GPUs. The VisionPro Deep Learning now supports TensorRT for the runtime API, boosting the processing speed of each tool. TensorRT for Runtime is only supported in High Detail tools. For details of TensorRT support, see API Changelog.

## Green Classify High Detail Batch Processing Support for Runtime

Speeding up processing of a VisionPro Deep Learning tool can be achieved processing multiple images at once. To implement this idea, a way that divides the entire images into multiple batches and processes them in batches is adopted. This is called "batch processing" newly introduced in VisionPro Deep Learning 3.0 for Green Classify High Detail. Using the Batch Processing API in runtime can speed up the Green Classify High Detail processing via API. For more details about the batch processing API, see API Changelog.

## View Browser - List Mode

The list mode of the View Browser is a handy tool that provides you an overlook over every view's information in detail. The existing View Browser is now called the grid mode, and you can switch back and forth between these 2 modes. The list mode presents an information table that offers much more details about a view's status including marking, label, sets, or a view's index. You also can star a view if it is an important view or write and edit a memo for each view. See View Browser - List Mode for details.

## Other Improvements & Changes

### UI/UX Improvements in Parameter Search

For both the auto and the manual mode, the full list of parameters are provided and you can enable or disable checkboxes to choose what parameters to be tested on the Search page. For High Detail tools, picking up the best parameter set is determined with the new validation loss formulation adopted in VisionPro Deep Learning 3.0. One another change is that the scale of validation loss now ranges from 0 to 1, replacing "from 0 to 100" scale. See Validation Set and Validation Loss (Red Analyze High Detail) and Validation Set and Validation Loss (Green Classify High Detail).

### GUI Refinement

As of VisionPro Deep Learning 3.0 release, the GUI of VisionPro Deep Learning is renovated adopting modern UI style and standards. The most remarkable change is that the View Browser is now divided into 2 modes: the grid mode and the list mode. See GUI and API for more details.

### Applying Label Change for Green Classify

If you change the label of a single view or create one for a single unlabeled view, the current tool (a tool already trained and processed once) will not be automatically reprocessed anymore. Instead, after modifying or creating labels as necessary, you need to process the current tool manually.

### Image Resize for Green Classify High Detail

The resize view (Resize Parameters on Green Classify High Detail Tool Parameters) UI/UX is improved to let you update the maximum buffer size (the maximum width and height of a view to be resized) whenever you want. You no longer need to use the inefficient hard-coded maximum buffer sizes in both the auto and the manual modes. Instead, you are guided to update it from the snapshot of the available GPU memory and the value of Network Model parameters, and this will help you make the most of GPU memory. For more details of the new image resizing, see Resize.

### Validation Set Indicator and Filter for Validation Set

The views that are included in the validation set of a High Detail tool are now have special a indicator displayed on Image Display Area. It indicates these views belong to the validation set among the images of the training set. Also, new keywords 'validated'/ 'not validated' are introduced for Display Filter and Database Overview Filter to display/exclude only the views included in the validation set. For more details, see Prepare Validation Set (Green Classify High Detail) and Prepare Validation Set (Red Analyze High Detail).

### Scale Change of Validation Loss

The scales Red Analyze High Detail and Green Classify High Detail validation loss are changed from "0 to 100" to "0 to 1." This change applies when you monitor the validation loss with loss graphs and use Parameter Search auto mode for both tools. See Loss Inspector and Auto Mode Workflow for more details.

### Image Processing Logic Change for Red Analyze High Detail

The processing logic for Red Analyze High Detail is changed by implementing its new architecture upgrade, and Red Analyze High Detail now processes images with a different method. If you want to use the image-wise processing logic, you can use it by enabling Whole Image Processing checkbox on Processing parameters. For more details about this change, see Configure Processing Parameters.

### Minimum Image Size Change for Green Classify High Detail

The minimum image size for Green Classify High Detail is relaxed from 96 x 96 to 64 x 64 (pixels). For more details, see Collect Images.

- Minimum Image Size for Training: 64 x 64

- Minimum Image Size for Processing: 64 x 64

### Training Logic Change with Patience Epochs and Minimum Epochs

Patience Epochs and Minimum Epochs in the Training parameters of High Detail tools are applied differently during training from VisionPro Deep Learning 3.0. For more details, see Training Parameter Details: Patience Epochs & Minimum Epochs.

### Designer Plug-in Support

In VisionPro Deep Learning 3.0, the Designer plug-in is restored with the support for the following new features introduced in VisionPro Deep Learning 3.0.

- TensorRT Optimization

- Whole Image Processing (Red Analyze High Detail)

For more details, see Using Runtime Workspace with Cognex Designer.

### Miscellaneous.

- Red Analyze sort filter keyword count(region!) in View Browser which sort the views by the number of labeled defect regions is changed to count(region![area > 0]).

Note:

- The VisionPro, Designer and VisionPro Deep Learning software require that a valid Cognex Security Dongle be installed directly to PCs running the software during all phases of operation (programming, processing, training, testing, etc.). Any attempts to temporarily remove, substitute, or share a Cognex Security Dongle may cause your system to operate incorrectly, and may result in the loss of data.

  When VisionPro Deep Learning is configured for the Client/Server functionality, and a computer has been configured as a server, the Cognex Security Dongle must be attached to the server, but the client(s) does not need a Cognex Security Dongle.

- Sample code is included with the VisionPro Deep Learning 3.0 installer. The resources for the sample code (VisionPro Deep Learning 3.0 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.