For large images, 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. can take time. In such cases, consider limiting 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. size using a 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. or try splitting the ROI.

The AI labeling tools require GPU resources. If you have one GPU, you cannot use the AI labeling tools while 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. or processing a tool. This limitation does not apply when using multiple GPUs.

If you encounter memory issues when using the AI labeling tools:

1. Go to Help and select Compute Devices.

2. Turn off the Optimized GPU Memory option.

3. Try using the AI labeling tools again.

If the issue persists, you might need to replace your GPU.