Tool and Images

EasyClassify is the deep learning classification library of Open eVision (EClassifier class).

Deep Learning Studio

To create a classification tool in Deep Learning Studio:

1. Start Deep Learning Studio.
2. Select EasyClassify in the New deep learning tool dialog.

Input image format and normalization

● The input image format must have the width, height and number of channels corresponding to the input of the neural network.
● By default, a classifier uses the image format of the first image inserted in the training dataset:
□ All other images are automatically reformatted (anisotropic rescaling and conversion between color and grayscale).
□ If EClassifier::SetEnableAutomaticImageReformat(false) is called, the classifier throws an exception when attempting to train or classify an image that does not have the correct image format.
● In Deep Learning Studio, you can set the input image format in the Tool properties and data augmentation tab.

● In the API, you can also set manually the input image format with the methods SetWidth, SetHeight and SetChannels (1 channel for grayscale images and 3 channels for color images).
● The input image format must have a resolution of at least 128 x 128 for the normal and the large capacity or 64 x 64 for the small capacity and at most 1024 x 1024.
For the best processing speed, use the lowest resolution at which your "objects of interest" are still recognizable.
□ If your original images are smaller than the minimum resolution, upscale them to a resolution higher or equal to 128 x 128.
□ If your original images are larger than the maximum resolution, lower the resolution:
- If the "objects of interest" are still recognizable, explicitly set the input image format of the classifier to this lower resolution.
- If the "objects of interest" are not recognizable, divide your original images into sub-windows and use these sub-windows to train the classifier and make predictions. This presents the additional advantage of localizing the "object of interest" inside the original image.

● The Capacity of the neural network (default: Normal) represents the quantity of information that it is capable of learning.
□ The small network is much smaller in memory and faster at inference.
□ The large network can handle more complex datasets. It is also better for datasets with a lot of noise.

In the API:

□ The capacity values are defined by the enumerate type EClassifierCapacity.
□ Use EClassifier::SetCapacity to set the capacity of your tool.

Histogram equalization

The classifier can also apply an histogram equalization to every input image:

□ In Deep Learning Studio, activate it in the image format controls in the Image properties and augmentation tab.
□ In the API, use EClassifier::SetEnableHistogramEqualization(true) to activate it.

Training

To train your tool, see Training a Deep Learning Tool.