Jul 8, 2024 · This paper addresses this gap by evaluating different CNN architectures using diverse publicly available texture datasets and custom datasets tailored for rock ...
Jan 1, 2024 · By evaluating the performance of different models in texture analysis, we seek to summarize the effectiveness of these models and understand ...
Aug 7, 2024 · The study highlights the efficacy of transfer learning in texture classification tasks and offers valuable perspectives on the performance of ...
Jul 18, 2024 · We observe that while CNNs trained on datasets like ImageNet prioritize texture-based features, they face challenges in nuanced texture-to- ...
Our findings underscore the need for further research to enhance CNNs' capabilities in texture analysis, particularly in the context of rock classification.
Aug 20, 2024 · Comprehensive Evaluation of ImageNet-Trained CNNs for Texture-Based Rock Classification · Dipendra Jee Mandal · Hilda Deborah · Tabita Anggraini ...
Oct 22, 2024 · We observe that while CNNs trained on datasets like ImageNet prioritize texture-based features, they face challenges in nuanced texture-to ...
We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence ...
Missing: Comprehensive Rock
Comprehensive Evaluation of ImageNet-Trained CNNs for Texture-Based Rock Classification ... Types of Rocks Quickly and Accurately Based on Lightweight CNNs ...
Using this dataset of 2D μCT images of core plugs, we applied convolutional neural networks (CNNs) pre-trained on ImageNet and fine-tuned them for rock ...