DOI: 10.5281/zenodo.7194300
title: COCO, LVIS, Open Images V4 classes mapping

description: This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes. COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes. We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using wordnet This repository contains the following files: <em>coco_classes_map.txt</em>, contains the mapping for the 80 coco classes <em>lvis_classes_map.txt</em>, contains the mapping for the 1460 coco classes <em>openimages_classes_map.txt</em>, contains the mapping for the 601 coco classes <em>classname_hyperset_definition.csv</em>, contains the final set of 1460 classes, their definition and hierarchy <em>all-classnames.xlsx</em>, contains a side-by-side view of all classes considered This mapping was used in VISIONE [Amato et al. 2021, Amato et al. 2022] that is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). For the object detection VISIONE uses three pre-trained models: VfNet [Zhang et al. 2021] (trained on COCO dataset), Mask R-CNN [He et al. 2017] (trained on LVIS), and a Faster R-CNN+Inception ResNet (trained on the Open Images V4). This is repository is released under a Creative Commons Attribution license, please cite the following paper if you use it in your work in any form: <pre>@inproceedings{amato2021visione, title={The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Debole, Franca and Falchi, Fabrizio and Gennaro, Claudio and Vadicamo, Lucia and Vairo, Claudio}, journal={Journal of Imaging}, volume={7}, number={5}, pages={76}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} } </pre> <em><strong>References:</strong></em> [Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. <em>MMM 2022. Lecture Notes in Computer Science</em>, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_52 [Amato et al. 2021] Amato, G., Bolettieri, P., Carrara, F., Debole, F., Falchi, F., Gennaro, C., Vadicamo, L. and Vairo, C., 2021. The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. <em>Journal of Imaging</em>, <em>7</em>(5), p.76. [Gupta et al.2019] Gupta, A., Dollar, P. and Girshick, R., 2019. Lvis: A dataset for large vocabulary instance segmentation. In <em>Proceedings of the IEEE/CVF conference on computer vision and pattern recognition</em> (pp. 5356-5364). [He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In <em>Proceedings of the IEEE international conference on computer vision</em> (pp. 2961-2969). [Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. <em>International Journal of Computer Vision</em>, <em>128</em>(7), pp.1956-1981. [Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In <em>European conference on computer vision</em> (pp. 740-755). Springer, Cham. [Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em> (pp. 8514-8523).