Open-CD: A Comprehensive Toolbox for Change Detection
We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules. The toolbox started from a series of open source general vision task tools, including OpenMMLab Toolkits, PyTorch Image Models, etc. It gradually evolves int...
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creator | Li, Kaiyu Jiang, Jiawei Codegoni, Andrea Han, Chengxi Deng, Yupeng Chen, Keyan Zheng, Zhuo Chen, Hao Zou, Zhengxia Shi, Zhenwei Fang, Sheng Meng, Deyu Wang, Zhi Cao, Xiangyong |
description | We present Open-CD, a change detection toolbox that contains a rich set of
change detection methods as well as related components and modules. The toolbox
started from a series of open source general vision task tools, including
OpenMMLab Toolkits, PyTorch Image Models, etc. It gradually evolves into a
unified platform that covers many popular change detection methods and
contemporary modules. It not only includes training and inference codes, but
also provides some useful scripts for data analysis. We believe this toolbox is
by far the most complete change detection toolbox. In this report, we introduce
the various features, supported methods and applications of Open-CD. In
addition, we also conduct a benchmarking study on different methods and
components. We wish that the toolbox and benchmark could serve the growing
research community by providing a flexible toolkit to reimplement existing
methods and develop their own new change detectors. Code and models are
available at \url{https://github.com/likyoo/open-cd}. Pioneeringly, this report
also includes brief descriptions of the algorithms supported in Open-CD, mainly
contributed by their authors. We sincerely encourage researchers in this field
to participate in this project and work together to create a more open
community. This toolkit and report will be kept updated. |
doi_str_mv | 10.48550/arxiv.2407.15317 |
format | Article |
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change detection methods as well as related components and modules. The toolbox
started from a series of open source general vision task tools, including
OpenMMLab Toolkits, PyTorch Image Models, etc. It gradually evolves into a
unified platform that covers many popular change detection methods and
contemporary modules. It not only includes training and inference codes, but
also provides some useful scripts for data analysis. We believe this toolbox is
by far the most complete change detection toolbox. In this report, we introduce
the various features, supported methods and applications of Open-CD. In
addition, we also conduct a benchmarking study on different methods and
components. We wish that the toolbox and benchmark could serve the growing
research community by providing a flexible toolkit to reimplement existing
methods and develop their own new change detectors. Code and models are
available at \url{https://github.com/likyoo/open-cd}. Pioneeringly, this report
also includes brief descriptions of the algorithms supported in Open-CD, mainly
contributed by their authors. We sincerely encourage researchers in this field
to participate in this project and work together to create a more open
community. This toolkit and report will be kept updated.</description><identifier>DOI: 10.48550/arxiv.2407.15317</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2024-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2407.15317$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2407.15317$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Kaiyu</creatorcontrib><creatorcontrib>Jiang, Jiawei</creatorcontrib><creatorcontrib>Codegoni, Andrea</creatorcontrib><creatorcontrib>Han, Chengxi</creatorcontrib><creatorcontrib>Deng, Yupeng</creatorcontrib><creatorcontrib>Chen, Keyan</creatorcontrib><creatorcontrib>Zheng, Zhuo</creatorcontrib><creatorcontrib>Chen, Hao</creatorcontrib><creatorcontrib>Zou, Zhengxia</creatorcontrib><creatorcontrib>Shi, Zhenwei</creatorcontrib><creatorcontrib>Fang, Sheng</creatorcontrib><creatorcontrib>Meng, Deyu</creatorcontrib><creatorcontrib>Wang, Zhi</creatorcontrib><creatorcontrib>Cao, Xiangyong</creatorcontrib><title>Open-CD: A Comprehensive Toolbox for Change Detection</title><description>We present Open-CD, a change detection toolbox that contains a rich set of
change detection methods as well as related components and modules. The toolbox
started from a series of open source general vision task tools, including
OpenMMLab Toolkits, PyTorch Image Models, etc. It gradually evolves into a
unified platform that covers many popular change detection methods and
contemporary modules. It not only includes training and inference codes, but
also provides some useful scripts for data analysis. We believe this toolbox is
by far the most complete change detection toolbox. In this report, we introduce
the various features, supported methods and applications of Open-CD. In
addition, we also conduct a benchmarking study on different methods and
components. We wish that the toolbox and benchmark could serve the growing
research community by providing a flexible toolkit to reimplement existing
methods and develop their own new change detectors. Code and models are
available at \url{https://github.com/likyoo/open-cd}. Pioneeringly, this report
also includes brief descriptions of the algorithms supported in Open-CD, mainly
contributed by their authors. We sincerely encourage researchers in this field
to participate in this project and work together to create a more open
community. This toolkit and report will be kept updated.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMjEw1zM0NTY052Qw9S9IzdN1drFScFRwzs8tKErNSM0rzixLVQjJz89Jyq9QSMsvUnDOSMxLT1VwSS1JTS7JzM_jYWBNS8wpTuWF0twM8m6uIc4eumAL4guKMnMTiyrjQRbFgy0yJqwCAOreMa8</recordid><startdate>20240721</startdate><enddate>20240721</enddate><creator>Li, Kaiyu</creator><creator>Jiang, Jiawei</creator><creator>Codegoni, Andrea</creator><creator>Han, Chengxi</creator><creator>Deng, Yupeng</creator><creator>Chen, Keyan</creator><creator>Zheng, Zhuo</creator><creator>Chen, Hao</creator><creator>Zou, Zhengxia</creator><creator>Shi, Zhenwei</creator><creator>Fang, Sheng</creator><creator>Meng, Deyu</creator><creator>Wang, Zhi</creator><creator>Cao, Xiangyong</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20240721</creationdate><title>Open-CD: A Comprehensive Toolbox for Change Detection</title><author>Li, Kaiyu ; Jiang, Jiawei ; Codegoni, Andrea ; Han, Chengxi ; Deng, Yupeng ; Chen, Keyan ; Zheng, Zhuo ; Chen, Hao ; Zou, Zhengxia ; Shi, Zhenwei ; Fang, Sheng ; Meng, Deyu ; Wang, Zhi ; Cao, Xiangyong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2407_153173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Li, Kaiyu</creatorcontrib><creatorcontrib>Jiang, Jiawei</creatorcontrib><creatorcontrib>Codegoni, Andrea</creatorcontrib><creatorcontrib>Han, Chengxi</creatorcontrib><creatorcontrib>Deng, Yupeng</creatorcontrib><creatorcontrib>Chen, Keyan</creatorcontrib><creatorcontrib>Zheng, Zhuo</creatorcontrib><creatorcontrib>Chen, Hao</creatorcontrib><creatorcontrib>Zou, Zhengxia</creatorcontrib><creatorcontrib>Shi, Zhenwei</creatorcontrib><creatorcontrib>Fang, Sheng</creatorcontrib><creatorcontrib>Meng, Deyu</creatorcontrib><creatorcontrib>Wang, Zhi</creatorcontrib><creatorcontrib>Cao, Xiangyong</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, Kaiyu</au><au>Jiang, Jiawei</au><au>Codegoni, Andrea</au><au>Han, Chengxi</au><au>Deng, Yupeng</au><au>Chen, Keyan</au><au>Zheng, Zhuo</au><au>Chen, Hao</au><au>Zou, Zhengxia</au><au>Shi, Zhenwei</au><au>Fang, Sheng</au><au>Meng, Deyu</au><au>Wang, Zhi</au><au>Cao, Xiangyong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Open-CD: A Comprehensive Toolbox for Change Detection</atitle><date>2024-07-21</date><risdate>2024</risdate><abstract>We present Open-CD, a change detection toolbox that contains a rich set of
change detection methods as well as related components and modules. The toolbox
started from a series of open source general vision task tools, including
OpenMMLab Toolkits, PyTorch Image Models, etc. It gradually evolves into a
unified platform that covers many popular change detection methods and
contemporary modules. It not only includes training and inference codes, but
also provides some useful scripts for data analysis. We believe this toolbox is
by far the most complete change detection toolbox. In this report, we introduce
the various features, supported methods and applications of Open-CD. In
addition, we also conduct a benchmarking study on different methods and
components. We wish that the toolbox and benchmark could serve the growing
research community by providing a flexible toolkit to reimplement existing
methods and develop their own new change detectors. Code and models are
available at \url{https://github.com/likyoo/open-cd}. Pioneeringly, this report
also includes brief descriptions of the algorithms supported in Open-CD, mainly
contributed by their authors. We sincerely encourage researchers in this field
to participate in this project and work together to create a more open
community. This toolkit and report will be kept updated.</abstract><doi>10.48550/arxiv.2407.15317</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Computer Vision and Pattern Recognition |
title | Open-CD: A Comprehensive Toolbox for Change Detection |
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