In the Train step, adjust the Training options.

1. Select the Training mode you want to use:

   - Deep Learning Lite

     - Few Sample: Default. Trainable with only a few images.

     - Accurate: Optimized for accuracy, requires more images to train.

   - Deep Learning Standard

     - Fast: Optimized for fast processing speed at runtime.

     - Accurate: Optimized for accuracy with large datasets.

     - Robust: Optimized to handle lighting and environmental variation.

     Note: Deep Learning Standard is currently not deployable on In-Sight vision sensors.

2. Adjust the training parameters as needed.

   Deep Learning Lite - Few Sample Training Parameters

   Training Parameter | Deep Learning Lite - Few Sample
   Optimize | If you check this box, the image sizes are optimized for the training process.
   Resize Parameters | Feature Size specifies the typical feature diameter in pixels. Adjust this setting to achieve a more accurate result by ignoring smaller image features than the specified Feature Size. The Feature Size approximates the size of a typical defect. For elongated defects, such as streaks, smears, scratches, and tears, the Feature Size approximates the width of the defect. For example, if the Feature Size is set to 16 pixels, any defect smaller than 16 pixels is ignored. For the best results, set the Feature Size slightly larger than the smallest defect you want to detect. Feature Size strongly influences processing time. A Feature Size of 100 is 100 times faster than a size of 10, while a Feature Size less than 8 usually does not yield good results. When setting the Feature Size, consider the equation in regards to processing time (Ptime): Ptime ~ F2

   Deep Learning Lite - Accurate Training Parameters

   Training Parameter | Deep Learning Lite - Accurate
   Epochs | Specifies the number of optimization iterations done during training. Use a lower value when your application has limited complexity, or when creating a lower quality model for testing different parameter settings. The tools typically need to see the training data about 40 times, which is the default setting. This setting is sufficient for most standard applications. If you use fewer epochs, the neural network model Each AI tool is a neural network model. A neural network model mimics the way biological neurons work in the human brain. The neural network model consists of interconnected layers of artifical neurons, called nodes, and they have multiple layers. Neural network models excel at tasks like image classification and pattern recognition. can get stuck in learning, or become unable to accurately solve the problem. However, if you use too many epochs, you risk overfitting The more training epochs that are used for training, the more accurately the network model responds to the training set images. After a certain amount of training, however, the improved performance of the network model on the training data is accompanied by poorer performance on unseen images.. Note: Temporarily reducing the Epochs parameter also makes the training time shorter.
   Optimize | If you check this box, the image sizes are optimized for the training process.
   Resize Parameters | Feature Size specifies the typical feature diameter in pixels. Adjust this setting to achieve a more accurate result by ignoring smaller image features than the specified Feature Size. The Feature Size approximates the size of a typical defect. For elongated defects, such as streaks, smears, scratches, and tears, the Feature Size approximates the width of the defect. For example, if the Feature Size is set to 16 pixels, any defect smaller than 16 pixels is ignored. For the best results, set the Feature Size slightly larger than the smallest defect you want to detect. Feature Size strongly influences processing time. A Feature Size of 100 is 100 times faster than a size of 10, while a Feature Size less than 8 usually does not yield good results. When setting the Feature Size, consider the equation in regards to processing time (Ptime): Ptime ~ F2

   Deep Learning Standard Training Parameters

   Training Parameter | Description
   Resize Parameters | Feature Size specifies the typical feature diameter in pixels. Adjust this setting to achieve a more accurate result by ignoring smaller image features than the specified Feature Size. The Feature Size approximates the size of a typical defect. For elongated defects, such as streaks, smears, scratches, and tears, the Feature Size approximates the width of the defect. For example, if the Feature Size is set to 16 pixels, any defect smaller than 16 pixels is ignored. For the best results, set the Feature Size slightly larger than the smallest defect you want to detect. Feature Size strongly influences processing time. A Feature Size of 100 is 100 times faster than a size of 10, while a Feature Size less than 8 usually does not yield good results. When setting the Feature Size, consider the equation in regards to processing time (Ptime): Ptime ~ F2 You can also choose other Resize Parameters Options and set them according to your preference. The list of parameters you can set: Fixed Area Resize Fixed Size Resize Fixed Size With Constant Padding Resize Maximum Area Resize None Resize Relative Size Resize Relative Size With Quantization Resize

   Augmentation Options
   Horizontal Flip | Performs flipping in horizontal direction. Use in cases where the location and the angles of the object are not strictly fixed.
   Vertical Flip | Performs flipping in vertical direction. Use in cases where the location and the angles of the object are not strictly fixed.
   Rotation 90° | Performs 90° rotation in clockwise direction. Note: If you select Horizontal Flip, Vertical Flip, and Rotation 90°, the tool performs 0°, 90°, 180°, and 270° rotations probabilistically.
   Rotation | Performs rotation between 0° to 45°. Note: Note: If you select Rotation 90°, Rotation, Horizontal Flip, and Vertical Flip, the tool performs random rotation between 0° to 360°.
   Luminance | Adjusts luminance by adding a random value to all channels. The random value follows uniform distribution within a range of - 255 to 255. Use when the images have inconsistent luminance because of the irregular lighting environment.
   Contrast | Adjusts contrast by multiplying a random value for all channels. The random value follows uniform distribution within a range of 0 to 2. Use when the images have inconsistent contrast because of the irregular lighting environment.
   Colorwise | Adjusts color by multiplying or adding different random values to each channel. Use this parameter with the Contrast or the Luminance parameter. The Colorwise setting makes the Contrast and Luminance parameters apply different random values to each color channel instead of the same value to all channels. Use when the images have inconsistent color tone because of the irregular lighting environment.
   Sharpness | Randomly sharpens the views by image filtering within a range of 0 to 2. Use when the image are too blurry.
   Blur | Randomly applies Gaussian Blur to the views. The random variable follows to the Gaussian Sigma Distribution within a range of 0 to 2. Use when the images are too sharp.
   Gradation | Randomly adjusts the gradation. Use when the images have inconsistent gradation because of the irregular lighting environment.
   Noise | Applies noise by multiplying a random value per pixel for all channels. The random value follows uniform distribution within a range of 0 to 2. Use when the images are distorted or contaminated by dust due to the deterioration of the optical equipment.
   Zoom In | Randomly zooms in from the center. The maximum zooming is 5/6 of the original size. The random variable follows uniform distribution. Use when the feature is an irregular size.
   Distortion | Applies a distortion by picking the points in the views and moving them. The number of points is the same or less than six. Use when the images are distorted due to the deterioration of the optical equipment.

3. To auto-assign roles to images, click Auto-Assign Roles. A pop-up window provides you with options for how to assign roles:

   See the Label section for more information.