  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.
