The Open Images Dataset V4 Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale

We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr with...

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Published in:International journal of computer vision Vol. 128; no. 7; pp. 1956 - 1981
Main Authors: Kuznetsova, Alina, Rom, Hassan, Alldrin, Neil, Uijlings, Jasper, Krasin, Ivan, Pont-Tuset, Jordi, Kamali, Shahab, Popov, Stefan, Malloci, Matteo, Kolesnikov, Alexander, Duerig, Tom, Ferrari, Vittorio
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2020
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ISSN:0920-5691, 1573-1405
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Abstract We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15 × more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning. We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images. We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.
AbstractList We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15 × more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning. We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images. We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.
Author Duerig, Tom
Popov, Stefan
Alldrin, Neil
Malloci, Matteo
Krasin, Ivan
Kamali, Shahab
Kuznetsova, Alina
Uijlings, Jasper
Pont-Tuset, Jordi
Ferrari, Vittorio
Rom, Hassan
Kolesnikov, Alexander
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  surname: Ferrari
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  organization: Google Research
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Keywords Ground-truth dataset
Visual relationship detection
Object detection
Image classification
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References Papadopoulos, D.P., Uijlings, J.R., Keller, F., & Ferrari, V. (2017). Extreme clicking for efficient object annotation. In ICCV.
Uijlings, J., Popov, S., & Ferrari, V. (2018). Revisiting knowledge transfer for training object class detectors. In CVPR.
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., & Zisserman, A. (2012). The PASCAL visual object classes challenge 2012 (VOC2012) results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html.
FelzenszwalbPGirshickRMcAllesterDRamananDObject detection with discriminatively trained part based modelsIEEE Transactions on Pattern Analysis and Machine Intelligence20103291627164510.1109/TPAMI.2009.167
Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (2018). Neural motifs: Scene graph parsing with global context. In CVPR.
Zhang, H., Kyaw, Z., Chang, S.F., & Chua, T.S. (2017a). Visual translation embedding network for visual relation detection. In CVPR.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In CVPR.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In CVPR.
Yao, B., & Fei-Fei, L. (2010). Modeling mutual context of object and human pose in human-object interaction activities. In CVPR.
AlexeBDeselaersTFerrariVMeasuring the objectness of image windowsIEEE Transactions on PAMI2012342189220210.1109/TPAMI.2012.28
Dai, B., Zhang, Y., & Lin, D. (2017). Detecting visual relationships with deep relational networks. In CVPR.
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI.
UijlingsJRRvan de SandeKEAGeversTSmeuldersAWMSelective search for object recognitionInternational Journal of Computer Vision201310415417110.1007/s11263-013-0620-5
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In NeurIPS.
Gupta, S., & Malik, J. (2015). Visual semantic role labeling. arXiv preprint arXiv:1505.04474.
Zhang, H., Kyaw, Z., Yu, J., & Chang, S.F. (2017b). PPR-FCN: weakly supervised visual relation detection via parallel pairwise R-FCN. In ICCV
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In CVPR.
ViolaPJonesMRobust real-time object detectionInternational Journal of Computer Vision200144
PrestASchmidCFerrariVWeakly supervised learning of interactions between humans and objectsIEEE Transactions on Pattern Analysis and Machine Intelligence20123460161410.1109/TPAMI.2011.158
Lin, T., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017). Focal loss for dense object detection. In ICCV.
Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., & Belongie, S. (2017). Learning from noisy large-scale datasets with minimal supervision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 839–847). http://openaccess.thecvf.com/content_cvpr_2017/papers/Veit_Learning_From_Noisy_CVPR_2017_paper.pdf.
Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona P, Ramanan, D., Zitnick, C.L., & Dollár, P. (2014). Microsoft COCO: Common objects in context. In ECCV.
Hinton, G. E., Vinyals, O., & Dean, J. (2014). Distilling the knowledge in a neural network. In NeurIPS.
Krizhevsky, A. (2009). Learning multiple layers of features from tiny images. Technical report, University of Toronto.
Fei-FeiLFergusRPeronaPOne-shot learning of object categoriesIEEE Transactions on Pattern Analysis and Machine Intelligence200628459461110.1109/TPAMI.2006.79
Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2018). Detecting visual relationships using box attention. arXiv:1807.02136.
QianNOn the momentum term in gradient descent learning algorithmsNeural Networks1999121145151146555910.1016/S0893-6080(98)00116-6
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., & Fei-fei, L. (2009). ImageNet: A large-scale hierarchical image database. In CVPR.
GuptaAKembhaviADavisLObserving human-object interactions: Using spatial and functional compatibility for recognitionIEEE Transactions on Pattern Analysis and Machine Intelligence2009311775178910.1109/TPAMI.2009.83
Felzenszwalb, P., Girshick, R., & McAllester, D. (2010a). Cascade object detection with deformable part models. In CVPR.
Gkioxari, G., Girshick, R., Dollár, P., & He, K. (2018). Detecting and recognizing human-object interactions. CVPR.
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML.
Liang, X., Lee, L., & Xing, E. P. (2017). Deep variation-structured reinforcement learning for visual relationship and attribute detection. In CVPR.
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017). Revisiting unreasonable effectiveness of data in deep learning era. In ICCV.
Su, H., Deng, J., & Fei-Fei, L. (2012). Crowdsourcing annotations for visual object detection. In AAAI Human Computation Workshop.
Liang, K., Guo, Y., Chang, H., & Chen, X. (2018). Visual relationship detection with deep structural ranking. In AAAI.
EveringhamMVan GoolLWilliamsCKIWinnJZissermanAThe PASCAL Visual Object Classes (VOC) ChallengeInternational Journal of Computer Vision20108830333810.1007/s11263-009-0275-4
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR.
KrishnaRZhuYGrothOJohnsonJHataKKravitzJChenSKalantidisYLiLJShammaDABernsteinMFei-FeiLVisual genome: Connecting language and vision using crowdsourced dense image annotationsIJCV201712313273364073810.1007/s11263-016-0981-7
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In NeurIPS.
Gao, C., Zou, Y., & Huang, J.B. (2018). iCAN: Instance-centric attention network for human-object interaction detection. In BMVC.
Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. In CVPR.
EveringhamMEslamiSvan GoolLWilliamsCWinnJZissermanAThe PASCAL visual object classes challenge: A retrospectiveInternational Journal of Computer Vision20151119813610.1007/s11263-014-0733-5
Lu, C., Krishna, R., Bernstein, M., & Fei-Fei, L. (2016). Visual relationship detection with language priors. In European Conference on Computer Vision.
Alexe, B., Deselaers, T., & Ferrari, V. (2010). What is an object? In CVPR.
Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., & Chen, L. (2018). Mobilenetv2: Inverted residuals and linear bottleneck. In CVPR.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., & Berg, A.C. (2016). SSD: Single shot multibox detector. In ECCV.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In CVPR.
Girshick, R. (2015). Fast R-CNN. In ICCV.
Peyre, J., Laptev, I., Schmid, C., & Sivic, J. (2017). Weakly-supervised learning of visual relations. In CVPR.
Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In NeurIPS.
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., & Murphy, K. (2017). Speed/accuracy trade-offs for modern convolutional object detectors. In CVPR.
Papadopoulos, D.P., Uijlings, J.R.R., Keller, F., & Ferrari, V. (2016). We don’t need no bounding-boxes: Training object class detectors using only human verification. In CVPR.
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., & Fei-Fei, L. (2015). ImageNet large scale visual recognition challenge. IJCV.
Viola, P., & Jones, M. (2001a). Rapid object detection using a boosted cascade of simple features. In CVPR.
Li, Y., Ouyang, W., Wang, X., & Tang, X. (2017). ViP-CNN: Visual phrase guided convolutional neural network. In CVPR.
Griffin, G., Holub, A., & Perona, P. (2007). The Caltech-256. Technical report, Caltech.
Xu, D., Zhu, Y., Choy, C., & Fei-Fei, L. (2017). Scene graph generation by iterative message passing. In Computer Vision and Pattern Recognition (CVPR).
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References_xml – reference: Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., & Murphy, K. (2017). Speed/accuracy trade-offs for modern convolutional object detectors. In CVPR.
– reference: Viola, P., & Jones, M. (2001a). Rapid object detection using a boosted cascade of simple features. In CVPR.
– reference: EveringhamMVan GoolLWilliamsCKIWinnJZissermanAThe PASCAL Visual Object Classes (VOC) ChallengeInternational Journal of Computer Vision20108830333810.1007/s11263-009-0275-4
– reference: Felzenszwalb, P., Girshick, R., & McAllester, D. (2010a). Cascade object detection with deformable part models. In CVPR.
– reference: FelzenszwalbPGirshickRMcAllesterDRamananDObject detection with discriminatively trained part based modelsIEEE Transactions on Pattern Analysis and Machine Intelligence20103291627164510.1109/TPAMI.2009.167
– reference: Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017). Revisiting unreasonable effectiveness of data in deep learning era. In ICCV.
– reference: Xu, D., Zhu, Y., Choy, C., & Fei-Fei, L. (2017). Scene graph generation by iterative message passing. In Computer Vision and Pattern Recognition (CVPR).
– reference: Yao, B., & Fei-Fei, L. (2010). Modeling mutual context of object and human pose in human-object interaction activities. In CVPR.
– reference: Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI.
– reference: UijlingsJRRvan de SandeKEAGeversTSmeuldersAWMSelective search for object recognitionInternational Journal of Computer Vision201310415417110.1007/s11263-013-0620-5
– reference: Lu, C., Krishna, R., Bernstein, M., & Fei-Fei, L. (2016). Visual relationship detection with language priors. In European Conference on Computer Vision.
– reference: Krizhevsky, A. (2009). Learning multiple layers of features from tiny images. Technical report, University of Toronto.
– reference: AlexeBDeselaersTFerrariVMeasuring the objectness of image windowsIEEE Transactions on PAMI2012342189220210.1109/TPAMI.2012.28
– reference: Liang, X., Lee, L., & Xing, E. P. (2017). Deep variation-structured reinforcement learning for visual relationship and attribute detection. In CVPR.
– reference: Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In NeurIPS.
– reference: Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., & Fei-fei, L. (2009). ImageNet: A large-scale hierarchical image database. In CVPR.
– reference: Hinton, G. E., Vinyals, O., & Dean, J. (2014). Distilling the knowledge in a neural network. In NeurIPS.
– reference: Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In CVPR.
– reference: Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In NeurIPS.
– reference: Li, Y., Ouyang, W., Wang, X., & Tang, X. (2017). ViP-CNN: Visual phrase guided convolutional neural network. In CVPR.
– reference: Papadopoulos, D.P., Uijlings, J.R.R., Keller, F., & Ferrari, V. (2016). We don’t need no bounding-boxes: Training object class detectors using only human verification. In CVPR.
– reference: Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (2018). Neural motifs: Scene graph parsing with global context. In CVPR.
– reference: Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In CVPR.
– reference: Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR.
– reference: Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. In CVPR.
– reference: KrishnaRZhuYGrothOJohnsonJHataKKravitzJChenSKalantidisYLiLJShammaDABernsteinMFei-FeiLVisual genome: Connecting language and vision using crowdsourced dense image annotationsIJCV201712313273364073810.1007/s11263-016-0981-7
– reference: Su, H., Deng, J., & Fei-Fei, L. (2012). Crowdsourcing annotations for visual object detection. In AAAI Human Computation Workshop.
– reference: PrestASchmidCFerrariVWeakly supervised learning of interactions between humans and objectsIEEE Transactions on Pattern Analysis and Machine Intelligence20123460161410.1109/TPAMI.2011.158
– reference: Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona P, Ramanan, D., Zitnick, C.L., & Dollár, P. (2014). Microsoft COCO: Common objects in context. In ECCV.
– reference: Peyre, J., Laptev, I., Schmid, C., & Sivic, J. (2017). Weakly-supervised learning of visual relations. In CVPR.
– reference: Griffin, G., Holub, A., & Perona, P. (2007). The Caltech-256. Technical report, Caltech.
– reference: Lin, T., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017). Focal loss for dense object detection. In ICCV.
– reference: Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2018). Detecting visual relationships using box attention. arXiv:1807.02136.
– reference: Zhang, H., Kyaw, Z., Chang, S.F., & Chua, T.S. (2017a). Visual translation embedding network for visual relation detection. In CVPR.
– reference: Dai, B., Zhang, Y., & Lin, D. (2017). Detecting visual relationships with deep relational networks. In CVPR.
– reference: GuptaAKembhaviADavisLObserving human-object interactions: Using spatial and functional compatibility for recognitionIEEE Transactions on Pattern Analysis and Machine Intelligence2009311775178910.1109/TPAMI.2009.83
– reference: Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., & Berg, A.C. (2016). SSD: Single shot multibox detector. In ECCV.
– reference: Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In NeurIPS.
– reference: Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., & Zisserman, A. (2012). The PASCAL visual object classes challenge 2012 (VOC2012) results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html.
– reference: Girshick, R. (2015). Fast R-CNN. In ICCV.
– reference: Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., & Fei-Fei, L. (2015). ImageNet large scale visual recognition challenge. IJCV.
– reference: Zhang, H., Kyaw, Z., Yu, J., & Chang, S.F. (2017b). PPR-FCN: weakly supervised visual relation detection via parallel pairwise R-FCN. In ICCV
– reference: Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In CVPR.
– reference: Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In CVPR.
– reference: Gao, C., Zou, Y., & Huang, J.B. (2018). iCAN: Instance-centric attention network for human-object interaction detection. In BMVC.
– reference: Uijlings, J., Popov, S., & Ferrari, V. (2018). Revisiting knowledge transfer for training object class detectors. In CVPR.
– reference: Papadopoulos, D.P., Uijlings, J.R., Keller, F., & Ferrari, V. (2017). Extreme clicking for efficient object annotation. In ICCV.
– reference: He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR.
– reference: Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., & Chen, L. (2018). Mobilenetv2: Inverted residuals and linear bottleneck. In CVPR.
– reference: Fei-FeiLFergusRPeronaPOne-shot learning of object categoriesIEEE Transactions on Pattern Analysis and Machine Intelligence200628459461110.1109/TPAMI.2006.79
– reference: Gkioxari, G., Girshick, R., Dollár, P., & He, K. (2018). Detecting and recognizing human-object interactions. CVPR.
– reference: EveringhamMEslamiSvan GoolLWilliamsCWinnJZissermanAThe PASCAL visual object classes challenge: A retrospectiveInternational Journal of Computer Vision20151119813610.1007/s11263-014-0733-5
– reference: ViolaPJonesMRobust real-time object detectionInternational Journal of Computer Vision200144
– reference: Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., & Belongie, S. (2017). Learning from noisy large-scale datasets with minimal supervision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 839–847). http://openaccess.thecvf.com/content_cvpr_2017/papers/Veit_Learning_From_Noisy_CVPR_2017_paper.pdf.
– reference: Gupta, S., & Malik, J. (2015). Visual semantic role labeling. arXiv preprint arXiv:1505.04474.
– reference: Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML.
– reference: Alexe, B., Deselaers, T., & Ferrari, V. (2010). What is an object? In CVPR.
– reference: QianNOn the momentum term in gradient descent learning algorithmsNeural Networks1999121145151146555910.1016/S0893-6080(98)00116-6
– reference: Liang, K., Guo, Y., Chang, H., & Chen, X. (2018). Visual relationship detection with deep structural ranking. In AAAI.
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Snippet We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The...
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Index Database
Publisher
StartPage 1956
SubjectTerms Artificial Intelligence
Computer Imaging
Computer Science
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Vision
Subtitle Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale
Title The Open Images Dataset V4
URI https://link.springer.com/article/10.1007/s11263-020-01316-z
Volume 128
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