# Recognize Objects with Green Classify

Distinguishing different objects under various viewing angles and lighting conditions is a common, yet difficult task. Approaches like pattern matching or color histogram comparison may be viable in certain cases. In general, however, they do not work reliably, are difficult to tune, and need to be carefully selected on a case-by-case basis by a vision expert.

In this tutorial, we will use theGreen Classify tool and its simple and intuitive interface to work on an image classification database containing 150 different objects. There are 24 images from different viewing angles for each object. The goal is to use a subset of images from each class to train, and then to test the resulting performance on the remaining, untrained images.

1. Add the images for the Classify tutorial.
2. Add a Green Classify tool.
3. Process the images (in other words, image segmentation Segmentation is the process of selecting a view from an image.).

4. Label the images according to the image filename, using the following regular expression: (\d*)_.*

   The images are given the following name, n_Ixcy.png, where n is the object number, composed of 1 to 3 characters, and x and y are numbers describing the incidence of light.

   We want to tag them according to the object number. To do this, we will use a regex (regular expression):

       (^.*)_.*
       select: ()
       all characters at the beginning: ^.*
       before '_': _
       and ignore the rest: .*

5. Now that the images are correctly tagged, it is possible to run the first 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.. Let's keep all the parameters at their default values.
6. Verify that the Training Set dialog is set to 50%.
7. Train the tool, using the images you imported.
8. Review the results.