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

1. Select a Training Mode:

- Deep Learning Lite - Few Sample (Default): Optimized for standardized objects. Requires only a few images to train.

- Deep Learning Lite - Accurate: Optimized for rotated and scaled objects. Requires more images to train.

2. Adjust the training parameters as needed.

   Training Parameters

   Training Parameter | Description
   Epochs | Note: Not applicable for Deep Learning Lite - Few Sample. 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.
   Model Health Folds | Note: Not applicable for Deep Learning Lite - Few Sample. Number of folds to use for cross-validation.
   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 feature that is needed for a good classification. For example, If the Feature Size is set to 16 pixels, any image feature smaller than 16 pixels is ignored for classification. 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 Resize Parameter Options determines which resize policy is applied. Default changes the size of the image to the optimal size for the tool and is therefore recommended. Feature Size allows manual adjustment, mainly for acceleration. However, this can degrade the quality of the result.

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.