Turkey has 40% of the world's marble potential with its marble reserves in more than 120 different colors and designs. In addition, it is a country with a high potential with nearly 20 granite reserves. The classification of stones such as marble and granite is quite difficult due to the similar color and pattern. This situation poses a major problem for sector employees and customers. In order to solve this problem, a method has been proposed to classify marble and granite types in Turkey. Data sets with four mobile phones and two cameras have been created in quarries and retail stores in ten cities. Lens and imaging technology diversity was provided with 6 cameras used. In addition to stone images with ideal and non-ideal pattern-color tones, images of stones exposed to improper storage conditions and photographic errors such as light and shadow were also included in the data set. As a result of the study, a new data set containing 24480 images of 102 stones in total, 88 of which are marble and 14 of which are granite, was created. The number of images was increased to 171360 with three different data augmentation methods. With the data set, 7 convolutional neural networks were trained using transfer learning method. Stratified Shuffle Split, K fold cross validation methods were used to prevent memorization in training and to increase the performance and reliability of networks. The classification accuracy rate of the most successful network is 97.4%.