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What is Evaluating CNNs for Rock Texture Classification about?

This paper evaluates the effectiveness of various CNN architectures pre-trained on ImageNet for texture-based rock classification, highlighting the role of texture perception in geology and computer vision. The study investigates 38 distinct models using transfer learning and fine-tuning techniques, revealing that while CNNs excel in texture feature extraction, they struggle with nuanced texture classification tasks. The findings emphasize the need for further research to enhance CNN capabilities in texture

Author

jagdalehari15

Language

EN