After the Label step, proceed to the Train step to train the model.

1. Adjust the Training options. The Training mode you selected in the Label step impacts the parameters available.

   Deep Learning Lite - Few Sample Training Parameters

   Choose between two training variants:

   - Accurate: Optimized for accuracy with large datasets.

   - Fast: Optimized for fast processing speed at runtime.

   Deep Learning Lite - Accurate Training Parameters

   Parameter | Description |
   Resize Parameters | Feature Size specifies the typical feature diameter in pixels. Resize it within the image to set a more accurate size. The Feature Size approximates the size of a typical located element. For elongated elements, the Feature Size approximates the width of the feature. 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 |
   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. |

2. 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.