{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:16:00Z","timestamp":1772842560038,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province","award":["2019KZDZX1012"],"award-info":[{"award-number":["2019KZDZX1012"]}]},{"name":"Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province","award":["202103000090"],"award-info":[{"award-number":["202103000090"]}]},{"name":"Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province","award":["61675003"],"award-info":[{"award-number":["61675003"]}]},{"name":"Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province","award":["61906074"],"award-info":[{"award-number":["61906074"]}]},{"name":"Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province","award":["2019A1515011276"],"award-info":[{"award-number":["2019A1515011276"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["2019KZDZX1012"],"award-info":[{"award-number":["2019KZDZX1012"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["202103000090"],"award-info":[{"award-number":["202103000090"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["61675003"],"award-info":[{"award-number":["61675003"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["61906074"],"award-info":[{"award-number":["61906074"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["2019A1515011276"],"award-info":[{"award-number":["2019A1515011276"]}]},{"name":"Key-Area Research and Development Program of Guangzhou","award":["2019KZDZX1012"],"award-info":[{"award-number":["2019KZDZX1012"]}]},{"name":"Key-Area Research and Development Program of Guangzhou","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"Key-Area Research and Development Program of Guangzhou","award":["202103000090"],"award-info":[{"award-number":["202103000090"]}]},{"name":"Key-Area Research and Development Program of Guangzhou","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Key-Area Research and Development Program of Guangzhou","award":["61675003"],"award-info":[{"award-number":["61675003"]}]},{"name":"Key-Area Research and Development Program of Guangzhou","award":["61906074"],"award-info":[{"award-number":["61906074"]}]},{"name":"Key-Area Research and Development Program of Guangzhou","award":["2019A1515011276"],"award-info":[{"award-number":["2019A1515011276"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2019KZDZX1012"],"award-info":[{"award-number":["2019KZDZX1012"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["202103000090"],"award-info":[{"award-number":["202103000090"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["61675003"],"award-info":[{"award-number":["61675003"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["61906074"],"award-info":[{"award-number":["61906074"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2019A1515011276"],"award-info":[{"award-number":["2019A1515011276"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019KZDZX1012"],"award-info":[{"award-number":["2019KZDZX1012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202103000090"],"award-info":[{"award-number":["202103000090"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61675003"],"award-info":[{"award-number":["61675003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906074"],"award-info":[{"award-number":["61906074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019A1515011276"],"award-info":[{"award-number":["2019A1515011276"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019KZDZX1012"],"award-info":[{"award-number":["2019KZDZX1012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202103000090"],"award-info":[{"award-number":["202103000090"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61675003"],"award-info":[{"award-number":["61675003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906074"],"award-info":[{"award-number":["61906074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019A1515011276"],"award-info":[{"award-number":["2019A1515011276"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2019KZDZX1012"],"award-info":[{"award-number":["2019KZDZX1012"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["202103000090"],"award-info":[{"award-number":["202103000090"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["61675003"],"award-info":[{"award-number":["61675003"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["61906074"],"award-info":[{"award-number":["61906074"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2019A1515011276"],"award-info":[{"award-number":["2019A1515011276"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Using unmanned aerial vehicle (UAV) real-time remote sensing to monitor diseased plants or abnormal areas of orchards from a low altitude perspective can greatly improve the efficiency and response speed of the patrol in smart orchards. The purpose of this paper is to realize the intelligence of the UAV terminal and make the UAV patrol orchard in real-time. The existing lightweight object detection algorithms are usually difficult to consider both detection accuracy and processing speed. In this study, a new lightweight model named Swin-T YOLOX, which consists of the advanced detection network YOLOX and the strong backbone Swin Transformer, was proposed. Model layer pruning technology was adopted to prune the multi-layer stacked structure of the Swin Transformer. A variety of data enhancement strategies were conducted to expand the dataset in the model training stage. The lightweight Swin-T YOLOX model was deployed to the embedded platform Jetson Xavier NX to evaluate its detection capability and real-time performance of the UAV patrol mission in the orchard. The research results show that, with the help of TensorRT optimization, the proposed lightweight Swin-T YOLOX network achieved 94.0% accuracy and achieved a detection speed of 40 fps on the embedded platform (Jetson Xavier NX) for patrol orchard missions. Compared to the original YOLOX network, the model accuracy has increased by 1.9%. Compared to the original Swin-T YOLOX, the size of the proposed lightweight Swin-T YOLOX has been reduced to two-thirds, while the model accuracy has slightly increased by 0.7%. At the same time, the detection speed of the model has reached 40 fps, which can be applied to the real-time UAV patrol in the orchard.<\/jats:p>","DOI":"10.3390\/rs14225806","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T04:08:40Z","timestamp":1668744520000},"page":"5806","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Real-Time UAV Patrol Technology in Orchard Based on the Swin-T YOLOX Lightweight Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Yubin","family":"Lan","sequence":"first","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}]},{"given":"Shaoming","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}]},{"given":"Hewen","family":"Du","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}]},{"given":"Yaqi","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-3443","authenticated-orcid":false,"given":"Xiaoling","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"ref_1","first-page":"279","article-title":"Current Status, Problems and Development Trend of the Wisdom Agriculture Research in China","volume":"44","author":"Wang","year":"2016","journal-title":"J. Anhui Agric. Sci."},{"key":"ref_2","first-page":"55","article-title":"Design of Small-scale Intelligent Orchard System","volume":"11","author":"Wang","year":"2021","journal-title":"Agric. Eng."},{"key":"ref_3","first-page":"223","article-title":"Research on Monitoring and Analysis System of Corn Growth in Precision Agriculture Based on Internet of Things","volume":"40","author":"Fan","year":"2018","journal-title":"J. Agric. Mech. Res."},{"key":"ref_4","first-page":"81","article-title":"Design of cruise inspection system for four-rotor autonomous aircraft in orchard","volume":"38","author":"Zhang","year":"2017","journal-title":"J. Chin. Agric. Mech."},{"key":"ref_5","first-page":"26","article-title":"Application of Quadrotor UAV in the Inspection System of Citrus Orchard","volume":"36","author":"Gao","year":"2016","journal-title":"Process Autom. Instrum."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104814","DOI":"10.1016\/j.compag.2019.05.023","article-title":"Vision-based monitoring of orchards with UAVs","volume":"163","author":"Nikolaos","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_8","first-page":"5658","article-title":"Research on remote sensing recognition of wild planted Lonicera japonica based on deep convolutional neural network","volume":"45","author":"Shi","year":"2020","journal-title":"China J. Chin. Mater. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3390\/agriengineering2020019","article-title":"Detection and location of dead trees with pine wilt disease based on deep learning and UAV remote sensing","volume":"2","author":"Deng","year":"2020","journal-title":"AgriEngineering"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mo, J., Lan, Y., and Yang, D. (2021). Deep learning-based instance segmentation method of litchi canopy from UAV-acquired images. Remote Sens., 13.","DOI":"10.3390\/rs13193919"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 20\u201323). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective Search for Object Recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201310). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (CVPR), Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_14","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_15","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_17","unstructured":"Farhadi, A., and Redmon, J. (2018, January 18\u201322). Yolov3: An incremental improvement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A.C. (2016, January 10\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_19","unstructured":"Zheng, G., Songtao, L., Feng, W., Zeming, L., and Jian, S. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_20","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_21","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. Proceedings of the International Conference on Learning Representations (ICLR), Online."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1109\/JSTSP.2019.2961233","article-title":"Structured pruning for efficient convolutional neural networks via incremental regularization","volume":"14","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., and Zhang, C. (2017, January 22\u201329). Learning efficient convolutional networks through network slimming. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref_25","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H.P. (2017, January 24\u201326). Pruning filters for efficient convnets. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Qi, P., Sha, E.H.M., and Zhuge, Q. (2021, January 22\u201323). Accelerating framework of transformer by hardware design and model compression co-optimization. Proceedings of the IEEE\/ACM International Conference On Computer Aided Design (ICCAD), Wuxi, China.","DOI":"10.1109\/ICCAD51958.2021.9643586"},{"key":"ref_27","unstructured":"Yu, S., Chen, T., and Shen, J. (2022, January 25\u201329). Unified visual transformer compression. Proceedings of the International Conference on Learning Representations (ICLR), Online."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hou, Z., and Kung, S.Y. (2022, January 27\u201328). Multi-dimensional model compression of vision transformer. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Taiyuan, China.","DOI":"10.1109\/ICME52920.2022.9859786"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3446640","article-title":"TPrune: Efficient transformer pruning for mobile devices","volume":"5","author":"Mao","year":"2021","journal-title":"ACM Transact. Cyber-Phys. Syst."},{"key":"ref_30","unstructured":"DeVries, T., and Taylor, G.W. (2017). Improved regularization of convolutional neural networks with cutout. arXiv."},{"key":"ref_31","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (May, January 30). Mixup: Beyond empirical risk minimization. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_32","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015, January 7\u201312). Distilling the knowledge in a neural network. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_33","unstructured":"Han, S., Mao, H., and Dally, W.J. (2016, January 2\u20134). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. Proceedings of the International Conference on Learning Representations (ICLR), SAN Juan, PR, USA."},{"key":"ref_34","unstructured":"Li, Z., Wallace, E., and Shen, S. (2020, January 13\u201318). Train big, then compress: Rethinking model size for efficient training and inference of transformers. Proceedings of the International Conference on International Conference on Machine Learning (ICML), Online."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5806\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:20:08Z","timestamp":1760145608000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":34,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225806"],"URL":"https:\/\/doi.org\/10.3390\/rs14225806","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,17]]}}}