# EasySegment - Detecting and Segmenting Defects **EasySegment** is the deep learning segmentation library of **eVision**. It contains 2 different modes: * The [***unsupervised***](easysegment_unsupervised.html.md) mode: * The tool is trained only with good images ([`EUnsupervisedSegmenter`](../../../doxygen_cpp/classeVision_1_1EasyDeepLearning_1_1EUnsupervisedSegmenter.html) [_.NET](../../../doxygen_cs/classeVision_1_1EasyDeepLearning_1_1EUnsupervisedSegmenter.html) [_Python](../../../doxygen_python/classeVision_1_1EasyDeepLearning_1_1EUnsupervisedSegmenter.html) class). * This mode does not require a ground truth segmentation and the creation of the dataset is thus much quicker than for the supervised mode. * This mode can detect unexpected defects while the supervised mode is only capable of detecting defects similar to those in the dataset. * The [***supervised***](easysegment_supervised.html.md) mode: * The tool is trained using the ground truth segmentation defined for the images ([`ESupervisedSegmenter`](../../../doxygen_cpp/classeVision_1_1EasyDeepLearning_1_1ESupervisedSegmenter.html) [_.NET](../../../doxygen_cs/classeVision_1_1EasyDeepLearning_1_1ESupervisedSegmenter.html) [_Python](../../../doxygen_python/classeVision_1_1EasyDeepLearning_1_1ESupervisedSegmenter.html) class). * This mode can detect and segment more types of defects with better accuracy than the unsupervised mode. It directly builds a model for the defects while the unsupervised mode builds a model of the good images and tries to detect variations from this model. * You can also use this mode to segment other types of objects than defects.