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# 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, HPE recommends cropping the image. If you crop an image, leave a gap around the object of at least 16 pixels.

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