Open topic with navigation

# Training Requirements

The performance of classification is generally better if:

- the classifier contains only a few object classes (but it must contain at least two classes).
- the object classes are dissimilar. For example, when training a 'field' class and a 'beach' class, the presence of clouds in the sky in both sets of training images might cause confusion between the classes.
- the object classes are trained with many images. Usually around 100 images are sufficient to train an object class. If the images in a class are very similar, fewer images might be sufficient.

- the training images are representative of the variation typically found within the object class. For example, to train a "dog" class, use images of dogs of different sizes, breeds, colors, and from different viewpoints.

- the training images contain little background or clutter around the object in the image.

- the longest dimension (width or height) of the training image is at least 500 pixels - smaller images might result in reduced accuracy.

  TIP:

  High-resolution images where the object covers a small proportion of the image make poor training images. If you have a large image showing the object and it can be cropped such that its longest dimension still exceeds 500 pixels, Micro Focus recommends cropping the image. If you crop an image, leave a gap around the object of at least 16 pixels.

© 2018 EntIT Software LLC, a Micro Focus company | Send documentation feedback to Micro Focus Close Send documentation feedback To open the configured email client on this computer, open an email window. Otherwise, copy the information below to a web mail client, and send this email to swpdl.idoldocsfeedback@microfocus.com. We appreciate your feedback! Product: Topic Title: Help Topic: Feedback:

_FT_HTML5_bannerTitle.htm