Household Garbage Classification: A Transfer Learning Based Method and a Benchmark.

Household garbage images are highly diverse in color, texture and geometry, which poses significant challenges to garbage classification. Deep convolutional neural network (DCNN) have recently achieved remarkable progress due to their ability to learn high-level feature representations. It usually requires a large number of labelled image data for training a DCNN model. However, there are few public and mature data sets concerned on household garbage images. This severely limits the progress of research and the state of the art is not entirely clear. To address this problem, we introduce a new benchmark data set for household garbage image classification. This data set is called 30 Types of Household Garbage Images (HGI-30), which contains 6′000 images of 30 household garbage types, with complex backgrounds, different resolutions, and complicated variations in sample, pose, illumination and background. The publicly available HGI-30 data set allows researchers to develop more accurate and robust methods for both household garbage image processing and interpretation analysis of household garbage object. We further study the classification problem on this data set and propose a transfer learning based method, also provide a performance analysis, which serves as baseline result on this benchmark.