# Resolved Issues

This section describes resolved issues in the VisionPro Deep Learning 4.1 release.

## Resolved Tool Issues

The following tool issues are resolved in VisionPro Deep Learning 4.1:

Issue Type | Description
General tool issue | Attempting to train multiple tools repeatedly resulted in an internal error.
Moving the Region of Interest (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.) outside the 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. after rotating it in the Edit ROI window caused a crash.
Exports failed with an error when 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. size was large.
Red Analyze tool issue | 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. process proceeded and terminated abnormally in Fast and Accurate mode, even when the image labeling Labeling is the process of annotating an image with "ground truth". Depending on the tool that you are using, labeling can take different forms. You label an image set for two reasons: to provide the information needed to train the tool and to allow you to measure and validate the performance of the trained tool against the ground truth. conditions were not met.
Error pop-up appeared during training in Standard type when adding masks to images larger than a certain size.
The auto threshold setting occasionally failed to apply.
The inverted mask was not immediately applied after tool chaining a Red Analyze tool.
The tooltip text for several parameters of the Standard Red Analyze tool displayed incorrectly.
Green Classify tool issue | The class names in the confusion matrix were not updated when switching between multiple Green Classify tools with two classes.
The tooltip text for several parameters of the Standard type Green Classify tool displayed incorrectly.

## Other Resolved Issues

The following issues are resolved in VisionPro Deep Learning 4.1:

Issue Type | Description
API documentation issue | The description for the heatmap section of IGreenView in the C# API documentation was displayed incorrectly.
Fixed the description for Anomaly Detection (Legacy type Red Analyze) in the tooltype enumeration section of the C# API documentation and added the appropriate explanation.
Designer issue | Error occurred while importing a runtime workspace created by inputting images in folder units from VisionPro Deep Learning into the Designer.
GPU memory issue | The GPU memory was not released after processing a Standard type tool in Cognex Deep Learning Studio.
Label checker issue | The label checker results did not fit within the ROI after resizing the ROI to be smaller than the image.
License issue | Processing using the runtime API failed with an error when using the Legacy type Red Analyze/Green Classify-only license.
VisionPro issue | The heatmap for the Green Classify tool did not display correctly in tool chains set to Blue Locate - Green Classify in VisionPro.