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MARVEL: A Large-Scale Image Dataset for Maritime Vessels

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Computer Vision – ACCV 2016 (ACCV 2016)

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Fine-grained visual categorization has recently received great attention as the volumes of the labelled datasets for classification of specific objects, such as cars, bird species, and aircrafts, have been increasing. The collection of large datasets has helped vision based classification approaches and led to significant improvements in performances of the state-of-the-art methods. Visual classification of maritime vessels is another important task assisting naval security and surveillance applications. In this work, we introduce a large-scale image dataset for maritime vessels, consisting of 2 million user uploaded images and their attributes including vessel identity, type, photograph category and year of built, collected from a community website. We categorize the images into 109 vessel type classes and construct 26 superclasses by combining heavily populated classes with a semi-automatic clustering scheme. For the analysis of our dataset, extensive experiments have been performed, involving four potentially useful applications; vessel classification, verification, retrieval, and recognition. We report encouraging results for each application. The introduced dataset is publicly available.

E. Gundogdu—Please contact the corresponding author to download the dataset and its metadata or visit

E. Gundogdu and B. Solmaz—These authors contributed equally to this work.

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    A negative pair indicates a pair of different vessel images, whereas a positive pair corresponds to a pair of vessel images belonging to a unique vessel.


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We would like to thank to Koray Akçay for his invaluable support and special consultancy for maritime vessels.

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Correspondence to Erhan Gundogdu .

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Gundogdu, E., Solmaz, B., Yücesoy, V., Koç, A. (2017). MARVEL: A Large-Scale Image Dataset for Maritime Vessels. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10115. Springer, Cham.

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