# Glossary

- auto mode
  The auto mode is a grid-search mode in the Cognex Deep Learning Parameter Search utility, which automatically optimizes the search schedule by adopting a policy that obtain the best results with minimizing the search time. Given a list of parameters and their values, auto mode stops training the loaded tool and moves on to training with another parameter values when it finds no reason for better results from the current parameter values. Only Standard type tools support auto mode.
- bootstrap labeling
  Bootstrap labeling is a fast labeling method, which involves labeling a small sample of images, training the tool, and reviewing the results. Accept views with correct markings to convert them into labels, and replace incorrect markings with correct labels.
- downsampling size
  The magnitude of downsampling that consists the downsampled version of a defect probability heatmap.
- epoch count
  Deep learning tools are trained by repeatedly supplying sampled image data as input to the network, processing the data, comparing the results with the expected ground truth, and then adjusting the network weights and trying again. The epoch count is the number of times that each input sample is processed through the network.
- 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).
- feature size
  The subjective size of the image features that you feel are most important for analyzing image content. The feature size determines the size of the image region used for sampling. Only used with Blue Locate, Blue Read, and Legacy type tools.
- ground truth
  Ground truth refers to the actual nature of the problem that is the target of a machine learning model, reflected by the relevant data sets associated with the use case in question. Supervised machine learning models are trained on labeled data that are considered for the model to identify patterns that predict those labels on new data.
- 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.
- 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.
- labeling error
  Labeling errors occur when the ground truth information that you supply is inaccurate. Typical examples include mis-labeling a character in OCR, failing to label all of the instances of a particular feature in all images, or inconsistently labeling the extent of defect regions. Labeling errors can dramatically worsen tool performance.
- loss
  The Loss refers to validation loss, which is a metric that shows how a tool performs on the validation set. Loss can have a value between 0 and 1. The VisionPro Deep Learning application calculates the Loss based on the errors the tool makes when processing the images in the validation set. During training, you can check the Loss in real-time using the Loss Inspector.
- manual mode
  Manual mode is a grid-search mode in the Cognex Deep Learning Parameter Search utility, which runs tests on every selected parameter and their value ranges.
- marking
  Image markings are annotations produced by the VisionPro Deep Learning tools. The markings produced by a tool are the "answers" that the tool obtained when it processed a specific image. You validate the performance of the tool by comparing the markings produced by the tool with the labels that you applied to the same image. As with labels, the specific markings produced depend on the tool.
- mask image
  A two-dimensional array of pixels that has the same size as a view. The pixel values in the mask image determine whether or not the corresponding pixels in the view are used for training or run-time processing.
- 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.
- neural network model
  Each VisionPro Deep Learning tool is a neural network. A neural network mimics the way biological neurans work in the human brain. The neural network consists of interconnected layers of artifical neurons, called nodes, and they have multiple layers. Neural networks excel at tasks like image classification and pattern recognition.
- overfitting
  Overfitting means that the tool is memorizing the data from the training images, which leads to lower performance when using unseen images.
- patch size
  The size of the square that divides each view into several chunks. Training (feature detection) and processing each view are executed based on each chunk.
- perturbation
  Perturbation is the process of improving the trained tool tolerance of part and image variation by simulating the effect of specific types of variation.
- 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.
- 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.
- 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.
- sampling density
  The amount by which the sampling point is moved in the image between samples, expressed in terms of the feature size. If the feature size is 100 and the sampling density is 1, the sampling location is advanced by 100 pixels between samples. A sampling density of 4 advances the sampling location by 25 pixels between samples.
- segmentation
  Segmentation is the process of selecting a view from an image.
- throughput
  Throughput refers to the total number of images that can be processed per unit of time. If your application can process multiple streams concurrently using different threads, it might be possible to improve system throughput, although individual tool processing is slower.
- 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.
- training fraction
  The fraction (percentage) of the images in the image set that are used for training. Using only part of the image set for training allows you to validate the performance of the trained tool on images that have not been seen before.
- training image set
  A collection of images of your specific application. A training image set represents images of a specific part or process acquired in a consistent way using the lighting, optical, and mechanical characteristics of your runtime system. The training image set includes images that represent the range of image appearances that you expect to see in normal operation.
- validation
  Validation refers to a process during the training of a tool that helps to evaluate performance. Validation is like a mock exam that the tool takes during the training phase, separate from the final test. For example, validation helps you to recognize overfitting and avoid wasting time when training a tool. If you recognize that the tool is overfitting, you can stop the training early.
- validation set
  The validation set is a set of images reserved for validation. The validation set is separate from the training set and test set. Since the validation set is separate, it allows you to evaluate how well the tool performs when using unseen images. The VisionPro Deep Learning application calculates a special metric, the Loss. The Loss is based on the performance of the tool on the validation set.
- 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.