# Few Sample Mode Green Classify Parameters

The parameters adjust how the tool is trained, and also how the tool processes statistical results. You can open and close the parameters side pane by selecting the tool and then clicking the % icon. You can access additional parameters that provide advanced features by selecting Expert Mode in the Help menu.

## Type and Mode Parameters

Parameter | Expert Mode | Description
Type | No | Specifies the type of 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.. Note: Few Sample mode is only available for Standard type Green Classify tools.
Mode | No | Specifies the mode of the neural network 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., which affects the time required for 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. and processing.

## Training Parameters

The Training tool parameters control the training process.

Note: If you change the training parameters of an already trained tool, you must retrain the tool.

Parameter | Expert Mode | Description
Epoch Count | Yes | 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 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. data about 100 times, which is the default setting, and is typically sufficient for most standard applications. If you use fewer epochs, the neural network might get stuck in learning, or become unable to accurately solve the problem. However, if you use too many epochs, you risk overfitting Overfitting means that the tool is memorizing the data from the training images, which leads to lower performance when using unseen images..
Training Set | No | Specifies the training data set used to create a neural network model. The system only extracts the features of the images that are included in the training set. Click Edit to open a dialog where you can specify the sample sets and the percentage of the labeled images used as training samples. The system selects the training images randomly each time you start training. You can train the tool in Few Sample mode with just one image in each class in the training set, while the other modes require four images in each class.

## Processing Parameters

The Processing parameters control the way the tool processes images. Processing with the same models always gives the same results. You do not need to retrain the tool after changing the Processing parameters. You can see the effect after reprocessing the database.

Parameter | Expert Mode | Description
Threshold | No | Specifies the minimum score that a 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. must have to be classified. Among the classes that have higher score than threshold value, the tool picks the class with the largest score. If all the classes have a score that is equal to or less than 0, the tool returns n/c (not-classified). Note: The default threshold is 0% because it calculates the score for each tag in a different way from Legacy mode. It is not recommended to adjust the threshold value.
Heat Map | No | Shows the area that the tool used as a clue when assigning a class. You can use Heat Map as a debugging tool after training. If the heat map shows a different area than the regions that you consider critical for the classification, it might be better to train the tool again. The heat maps appears directly in the image viewer if you enable both the Heat Map parameter and the Heat Map option in the overlay checkbox. Note: The size of workspace can increase if you enable the Heat Map parameter. To make the workspace smaller, process the tool again without the heat map again and save the workspace.
Speed Optimization | No | Provides options to boost the processing speed of the tool using NVIDIA TensorRT: Basic: enhances processing speed, but lowers the performance of the Green Classify tool. Int8: provides even more speed improvement with more performance reduction. The performance degradation depends on the data. There is no specific pattern of data to be used for each option. Note: You must optimize the Speed Optimization parameter with the API for the runtime environment. For more information, see NVIDIA TensorRT Support for Runtime API.