# Training Workflow

1. Collect Images

   Follow the best practices for image quality in standard machine vision applications, where contrast is key. Within the image, defects and features must be human-distinguishable. Control all possible variables, such as consistent lighting, working distance, camera trigger timing, etc.

2. Label

   This establishes the 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. for your tools, what is good or bad, what is a 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). of interest, what a character is, what type of thing is in the image. It is important that you label all the views, and 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. must be accurate.

3. Set Tool Parameters

   The Deep Learning tool parameters adjust how the network trains and processes images. The most common tool parameters to adjust are the following:

   - Feature Size/Patch Size
   - Training Set
   - Perturbation parameters
   - Sampling Density

   Typically, the default settings of the parameters perform well against most image sets. Try 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. without adjusting any parameters beside Feature Size first.

4. Training

5. Review Markings

   Markings represent Deep Learning's results for each image, and have unique graphic representations for each tool. Labels are generated by the developer. Markings are generated by Deep Learning.

6. Evaluate Results

   Additional tool results are presented in the Database Overview panel. For each tool this includes the tool's processing time, scores and other statistical analysis.