# Evaluating Deep Convolutional Neural Networks for Material Classification Source: [Evaluating Deep Convolutional Neural Networks for Material Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:35:17.063Z Authors: Grigorios Kalliatakis, Georgios Stamatiadis, Shoaib Ehsan, Ales Leonardis, Juergen Gall, Anca Sticlaru, Klaus D. McDonald-Maier Published: 2017-03-12T11:29:00.000Z ## Abstract Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention. Following the recent remarkable results achieved for image classification and object detection utilising Convolutional Neural Networks (CNNs), we empirically study material classification of everyday objects employing these techniques. More specifically, we conduct a rigorous evaluation of how state-of-the art CNN architectures compare on a common ground over widely used material databases. Experimental results on three challenging material databases show that the best performing CNN architectures can achieve up to 94.99\\% mean average precision when classifying materials. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()