CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

Abstract

Recent results indicate that the generic descriptors ex-tracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of exper-iments conducted for different recognition tasks using the publicly available code and model of the OverFeat net-work which was trained to perform object classification on ILSVRC13. We use features extracted from the OverFeat network as a generic image representation to tackle the di-verse range of recognition tasks of object image classifica-tion, scene recognition, fine grained recognition, attribute detection and image retrieval applied to a diverse set of datasets. We selected these tasks and datasets as they grad-ually move further away from the original task and data the OverFeat network was trained to solve. Remarkably we report better or competitive results compared to the state-of-the-art in all the tasks on various datasets. The results are achieved using a linear SVM classifier applied to a fea-ture representation of size 4096 extracted from a layer in the net. The results strongly suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual classification tasks. 1.

Keywords

Affiliated Institutions

• KTH Royal Institute of Technology SE

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