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Deep Computer Vision for the Detection of Tantalum and Niobium Fragments in High Entropy Alloys

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Document pages: 5 pages

Abstract: Deep Computer Vision is capable of doing object detection and image classification task. In an image classification tasks, the particular system receives some input image and the system is aware of some predetermined set of categories or labels. There are some fixed set of category labels and the job of the computer is to look at the picture and assign it a fixed category label. Convolutional Neural Network (CNN) has gained wide popularity in the field of pattern recognition and machine learning. In our present work, we have constructed a Convolutional Neural Network (CNN) for the identification of the presence of tantalum and niobium fragments in a High Entropy Alloy (HEA). The results showed 100 accuracy while testing the given dataset.

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