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GitHub - lhoyer/HRDA: [ECCV22] Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
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class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex 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1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> 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data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="product-explore-heading"> 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0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"blog","context":"product","tag":"link","label":"blog_link_product_navbar"}" href="https://github.blog"> Blog <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 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data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a 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</button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"enterprise_platform","context":"enterprise","tag":"link","label":"enterprise_platform_link_enterprise_navbar"}" href="/enterprise"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-stack color-fg-subtle mr-3"> <path d="M11.063 1.456a1.749 1.749 0 0 1 1.874 0l8.383 5.316a1.751 1.751 0 0 1 0 2.956l-8.383 5.316a1.749 1.749 0 0 1-1.874 0L2.68 9.728a1.751 1.751 0 0 1 0-2.956Zm1.071 1.267a.25.25 0 0 0-.268 0L3.483 8.039a.25.25 0 0 0 0 .422l8.383 5.316a.25.25 0 0 0 .268 0l8.383-5.316a.25.25 0 0 0 0-.422Z"></path><path d="M1.867 12.324a.75.75 0 0 1 1.035-.232l8.964 5.685a.25.25 0 0 0 .268 0l8.964-5.685a.75.75 0 0 1 .804 1.267l-8.965 5.685a1.749 1.749 0 0 1-1.874 0l-8.965-5.685a.75.75 0 0 1-.231-1.035Z"></path><path d="M1.867 16.324a.75.75 0 0 1 1.035-.232l8.964 5.685a.25.25 0 0 0 .268 0l8.964-5.685a.75.75 0 0 1 .804 1.267l-8.965 5.685a1.749 1.749 0 0 1-1.874 0l-8.965-5.685a.75.75 0 0 1-.231-1.035Z"></path> </svg> <div> <div class="color-fg-default h4">Enterprise platform</div> AI-powered developer platform </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="enterprise-available-add-ons-heading">Available add-ons</span> <ul class="list-style-none f5" aria-labelledby="enterprise-available-add-ons-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex 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2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 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6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="C7sFoiM26Kz0heW3W58cWBDreu265jbwHy3bXmhmxSfccoN9WTOqvdFV6T+RWJYoalxr2LJnM5JLRQxdJvDQzQ==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" 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Domain-Adaptive Semantic Segmentation\u003c/h2\u003e\u003ca id=\"user-content-hrda-context-aware-high-resolution-domain-adaptive-semantic-segmentation\" class=\"anchor\" aria-label=\"Permalink: HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation\" href=\"#hrda-context-aware-high-resolution-domain-adaptive-semantic-segmentation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eby \u003ca href=\"https://lhoyer.github.io/\" rel=\"nofollow\"\u003eLukas Hoyer\u003c/a\u003e, \u003ca href=\"https://vas.mpi-inf.mpg.de/dengxin/\" rel=\"nofollow\"\u003eDengxin Dai\u003c/a\u003e, and \u003ca href=\"https://scholar.google.de/citations?user=TwMib_QAAAAJ\u0026amp;hl=en\" rel=\"nofollow\"\u003eLuc Van Gool\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/pdf/2204.13132\" rel=\"nofollow\"\u003e[ECCV22 Paper]\u003c/a\u003e\u003c/strong\u003e\n\u003cstrong\u003e\u003ca href=\"https://arxiv.org/pdf/2304.13615.pdf\" rel=\"nofollow\"\u003e[Extension Paper]\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e🔔 \u003cstrong\u003eNews:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2024-07-03] We are happy to announce that our work \u003ca href=\"https://github.com/google-research/semivl\"\u003eSemiVL\u003c/a\u003e on semi-supervised semantic segmentation with vision-language guidance was accepted at \u003cstrong\u003eECCV24\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e[2024-07-03] We are happy to announce that our follow-up work \u003ca href=\"https://dginstyle.github.io/\" rel=\"nofollow\"\u003eDGInStyle\u003c/a\u003e on image diffusion for domain-generalizable semantic segmentation was accepted at \u003cstrong\u003eECCV24\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e[2023-09-26] We are happy to announce that our \u003ca href=\"https://arxiv.org/pdf/2304.13615.pdf\" rel=\"nofollow\"\u003eExtension Paper\u003c/a\u003e on domain generalization and clear-to-adverse-weather UDA was accapted at \u003cstrong\u003ePAMI\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e[2023-08-25] We are happy to announce that our follow-up work \u003ca href=\"https://github.com/susaha/edaps\"\u003eEDAPS\u003c/a\u003e on panoptic segmentation UDA was accepted at \u003cstrong\u003eICCV23\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e[2023-04-27] We further extend HRDA to domain generalization and clear-to-adverse-weather UDA in the \u003ca href=\"https://arxiv.org/pdf/2304.13615.pdf\" rel=\"nofollow\"\u003eExtension Paper\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2023-02-28] We are happy to announce that our follow-up work \u003ca href=\"https://github.com/lhoyer/MIC\"\u003eMIC\u003c/a\u003e on context-enhanced UDA was accepted at \u003cstrong\u003eCVPR23\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e[2022-07-05] We are happy to announce that HRDA was accepted at \u003cstrong\u003eECCV22\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUnsupervised domain adaptation (UDA)\u003c/strong\u003e aims to adapt a model trained on\nsynthetic data to real-world data without requiring expensive annotations of\nreal-world images. As UDA methods for semantic segmentation are usually GPU\nmemory intensive, most \u003cstrong\u003eprevious methods operate only on downscaled images\u003c/strong\u003e.\nWe question this design as low-resolution predictions often fail to preserve\nfine details. The alternative of training with random crops of high-resolution\nimages alleviates this problem but falls short in capturing long-range,\ndomain-robust context information.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTherefore, we propose \u003cstrong\u003eHRDA\u003c/strong\u003e, a multi-resolution training approach for UDA,\nthat combines the strengths of small high-resolution crops to \u003cstrong\u003epreserve fine\nsegmentation details\u003c/strong\u003e and large low-resolution crops to \u003cstrong\u003ecapture long-range\ncontext dependencies\u003c/strong\u003e with a learned scale attention, while maintaining a\n\u003cstrong\u003emanageable GPU memory\u003c/strong\u003e footprint.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/lhoyer/HRDA/blob/master/resources/hrda_overview.png\"\u003e\u003cimg src=\"/lhoyer/HRDA/raw/master/resources/hrda_overview.png\" alt=\"HRDA Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHRDA enables adapting small objects and preserving fine segmentation details.\nIt significantly improves the state-of-the-art performance \u003cstrong\u003eby 5.5 mIoU for\nGTA→Cityscapes\u003c/strong\u003e and \u003cstrong\u003eby 4.9 mIoU for Synthia→Cityscapes\u003c/strong\u003e, resulting in an\nunprecedented performance of 73.8 and 65.8 mIoU, respectively.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/lhoyer/HRDA/blob/master/resources/uda_over_time_hrda.png\"\u003e\u003cimg src=\"/lhoyer/HRDA/raw/master/resources/uda_over_time_hrda.png\" alt=\"UDA over time\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe more detailed domain-adaptive semantic segmentation of HRDA, compared to\nthe previous state-of-the-art UDA method DAFormer, can also be observed in\nexample predictions from the Cityscapes validation set.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/lhoyer/HRDA/blob/master/resources/demo.gif\"\u003e\u003cimg src=\"/lhoyer/HRDA/raw/master/resources/demo.gif\" alt=\"Demo\" data-animated-image=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdetails open=\"\" class=\"details-reset border rounded-2\"\u003e\n \u003csummary class=\"px-3 py-2\"\u003e\n \u003csvg aria-hidden=\"true\" height=\"16\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" data-view-component=\"true\" class=\"octicon octicon-device-camera-video\"\u003e\n \u003cpath d=\"M16 3.75v8.5a.75.75 0 0 1-1.136.643L11 10.575v.675A1.75 1.75 0 0 1 9.25 13h-7.5A1.75 1.75 0 0 1 0 11.25v-6.5C0 3.784.784 3 1.75 3h7.5c.966 0 1.75.784 1.75 1.75v.675l3.864-2.318A.75.75 0 0 1 16 3.75Zm-6.5 1a.25.25 0 0 0-.25-.25h-7.5a.25.25 0 0 0-.25.25v6.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-6.5ZM11 8.825l3.5 2.1v-5.85l-3.5 2.1Z\"\u003e\u003c/path\u003e\n\u003c/svg\u003e\n \u003cspan aria-label=\"Video description HRDA.Slider.Demo.mp4\" class=\"m-1\"\u003eHRDA.Slider.Demo.mp4\u003c/span\u003e\n \u003cspan class=\"dropdown-caret\"\u003e\u003c/span\u003e\n \u003c/summary\u003e\n\n \u003cvideo src=\"https://private-user-images.githubusercontent.com/1277888/181128057-27b8039f-a4c9-4f6d-9aa8-9b7f364d8921.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.CZ2dbzfFeKQHv302bt6zelcf6y7VF8NrZosF-23VvCo\" data-canonical-src=\"https://private-user-images.githubusercontent.com/1277888/181128057-27b8039f-a4c9-4f6d-9aa8-9b7f364d8921.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.CZ2dbzfFeKQHv302bt6zelcf6y7VF8NrZosF-23VvCo\" controls=\"controls\" muted=\"muted\" class=\"d-block rounded-bottom-2 border-top width-fit\" style=\"max-height:640px; min-height: 200px\"\u003e\n\n \u003c/video\u003e\n\u003c/details\u003e\n\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/lhoyer/HRDA/blob/master/resources/color_palette.png\"\u003e\u003cimg src=\"/lhoyer/HRDA/raw/master/resources/color_palette.png\" alt=\"Color Palette\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHRDA can be further \u003cstrong\u003eextended to domain generalization\u003c/strong\u003e lifting the requirement\nof access to target images. Also in domain generalization,\nHRDA significantly improves the state-of-the-art performance by \u003cstrong\u003e+4.2 mIoU\u003c/strong\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor more information on HRDA, please check our\n\u003ca href=\"https://arxiv.org/pdf/2204.13132\" rel=\"nofollow\"\u003e[ECCV Paper]\u003c/a\u003e and the \u003ca href=\"https://arxiv.org/pdf/2304.13615.pdf\" rel=\"nofollow\"\u003e[Extension Paper]\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you find HRDA useful in your research, please consider citing:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@InProceedings{hoyer2022hrda,\n title={{HRDA}: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation},\n author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc},\n booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},\n pages={372--391},\n year={2022}\n}\n\n@Article{hoyer2024domain,\n title={Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation},\n author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc},\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)}, \n year={2024},\n volume={46},\n number={1},\n pages={220-235},\n doi={10.1109/TPAMI.2023.3320613}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@InProceedings{hoyer2022hrda,\n title={{HRDA}: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation},\n author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc},\n booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},\n pages={372--391},\n year={2022}\n}\n\n@Article{hoyer2024domain,\n title={Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation},\n author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc},\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)}, \n year={2024},\n volume={46},\n number={1},\n pages={220-235},\n doi={10.1109/TPAMI.2023.3320613}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eComparison with SOTA UDA\u003c/h2\u003e\u003ca id=\"user-content-comparison-with-sota-uda\" class=\"anchor\" aria-label=\"Permalink: Comparison with SOTA UDA\" href=\"#comparison-with-sota-uda\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHRDA significantly outperforms previous works on several UDA benchmarks.\nThis includes synthetic-to-real adaptation on GTA→Cityscapes and\nSynthia→Cityscapes as well as clear-to-adverse-weather adaptation on\nCityscapes→ACDC and Cityscapes→DarkZurich.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003eGTA→CS(val)\u003c/th\u003e\n\u003cth\u003eSynthia→CS(val)\u003c/th\u003e\n\u003cth\u003eCS→ACDC(test)\u003c/th\u003e\n\u003cth\u003eCS→DarkZurich(test)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eADVENT [1]\u003c/td\u003e\n\u003ctd\u003e45.5\u003c/td\u003e\n\u003ctd\u003e41.2\u003c/td\u003e\n\u003ctd\u003e32.7\u003c/td\u003e\n\u003ctd\u003e29.7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBDL [2]\u003c/td\u003e\n\u003ctd\u003e48.5\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003ctd\u003e37.7\u003c/td\u003e\n\u003ctd\u003e30.8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFDA [3]\u003c/td\u003e\n\u003ctd\u003e50.5\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003ctd\u003e45.7\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDACS [4]\u003c/td\u003e\n\u003ctd\u003e52.1\u003c/td\u003e\n\u003ctd\u003e48.3\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProDA [5]\u003c/td\u003e\n\u003ctd\u003e57.5\u003c/td\u003e\n\u003ctd\u003e55.5\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMGCDA [6]\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003ctd\u003e48.7\u003c/td\u003e\n\u003ctd\u003e42.5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDANNet [7]\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003ctd\u003e--\u003c/td\u003e\n\u003ctd\u003e50.0\u003c/td\u003e\n\u003ctd\u003e45.2\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDAFormer (Ours) [8]\u003c/td\u003e\n\u003ctd\u003e68.3\u003c/td\u003e\n\u003ctd\u003e60.9\u003c/td\u003e\n\u003ctd\u003e55.4*\u003c/td\u003e\n\u003ctd\u003e53.8*\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHRDA (Ours)\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e73.8\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e65.8\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e68.0\u003c/strong\u003e*\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e55.9\u003c/strong\u003e*\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003e* New results of our \u003ca href=\"https://arxiv.org/pdf/2304.13615.pdf\" rel=\"nofollow\"\u003eextension paper\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eReferences:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eVu et al. \"Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation\" in CVPR 2019.\u003c/li\u003e\n\u003cli\u003eLi et al. \"Bidirectional learning for domain adaptation of semantic segmentation\" in CVPR 2019.\u003c/li\u003e\n\u003cli\u003eYang et al. \"Fda: Fourier domain adaptation for semantic segmentation\" in CVPR 2020.\u003c/li\u003e\n\u003cli\u003eTranheden et al. \"Dacs: Domain adaptation via crossdomain mixed sampling\" in WACV 2021.\u003c/li\u003e\n\u003cli\u003eZhang et al. \"Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation\" in CVPR 2021.\u003c/li\u003e\n\u003cli\u003eSakaridis et al. \"Map-guided curriculum domain adaptation and uncertaintyaware evaluation for semantic nighttime image segmentation\" in TPAMI, 2020.\u003c/li\u003e\n\u003cli\u003eWu et al. \"DANNet: A one-stage domain adaptation network for unsupervised nighttime semantic segmentation\" in CVPR, 2021.\u003c/li\u003e\n\u003cli\u003eHoyer et al. \"DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation\" in CVPR, 2022.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eComparison with SOTA Domain Generalization (DG)\u003c/h2\u003e\u003ca id=\"user-content-comparison-with-sota-domain-generalization-dg\" class=\"anchor\" aria-label=\"Permalink: Comparison with SOTA Domain Generalization (DG)\" href=\"#comparison-with-sota-domain-generalization-dg\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHRDA and DAFormer significantly outperform previous works on domain generalization from GTA to real street scenes.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDG Method\u003c/th\u003e\n\u003cth\u003eCityscapes\u003c/th\u003e\n\u003cth\u003eBDD100K\u003c/th\u003e\n\u003cth\u003eMapillary\u003c/th\u003e\n\u003cth\u003eAvg.\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eIBN-Net [1,5]\u003c/td\u003e\n\u003ctd\u003e37.37\u003c/td\u003e\n\u003ctd\u003e34.21\u003c/td\u003e\n\u003ctd\u003e36.81\u003c/td\u003e\n\u003ctd\u003e36.13\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDRPC [2]\u003c/td\u003e\n\u003ctd\u003e42.53\u003c/td\u003e\n\u003ctd\u003e38.72\u003c/td\u003e\n\u003ctd\u003e38.05\u003c/td\u003e\n\u003ctd\u003e39.77\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISW [3,5]\u003c/td\u003e\n\u003ctd\u003e37.20\u003c/td\u003e\n\u003ctd\u003e33.36\u003c/td\u003e\n\u003ctd\u003e35.57\u003c/td\u003e\n\u003ctd\u003e35.38\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSAN-SAW [4]\u003c/td\u003e\n\u003ctd\u003e45.33\u003c/td\u003e\n\u003ctd\u003e41.18\u003c/td\u003e\n\u003ctd\u003e40.77\u003c/td\u003e\n\u003ctd\u003e42.43\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSHADE [5]\u003c/td\u003e\n\u003ctd\u003e46.66\u003c/td\u003e\n\u003ctd\u003e43.66\u003c/td\u003e\n\u003ctd\u003e45.50\u003c/td\u003e\n\u003ctd\u003e45.27\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDAFormer (Ours) [6]\u003c/td\u003e\n\u003ctd\u003e52.65*\u003c/td\u003e\n\u003ctd\u003e47.89*\u003c/td\u003e\n\u003ctd\u003e54.66*\u003c/td\u003e\n\u003ctd\u003e51.73*\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHRDA (Ours)\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e57.41*\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e49.11*\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e61.16*\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e55.90*\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003e* New results of our \u003ca href=\"https://arxiv.org/pdf/2304.13615.pdf\" rel=\"nofollow\"\u003eextension paper\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eReferences:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003ePan et al. \"Two at once: Enhancing learning and generalization capacities via IBN-Net\" in ECCV, 2018.\u003c/li\u003e\n\u003cli\u003eYue et al. \"Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data\" ICCV, 2019.\u003c/li\u003e\n\u003cli\u003eChoi et al. \"RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening\" in CVPR, 2021.\u003c/li\u003e\n\u003cli\u003ePeng et al. \"Semantic-aware domain generalized segmentation\" in CVPR, 2022.\u003c/li\u003e\n\u003cli\u003eZhao et al. \"Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation\" in ECCV, 2022.\u003c/li\u003e\n\u003cli\u003eHoyer et al. \"DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation\" in CVPR, 2022.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSetup Environment\u003c/h2\u003e\u003ca id=\"user-content-setup-environment\" class=\"anchor\" aria-label=\"Permalink: Setup Environment\" href=\"#setup-environment\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor this project, we used python 3.8.5. We recommend setting up a new virtual\nenvironment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m venv ~/venv/hrda\nsource ~/venv/hrda/bin/activate\"\u003e\u003cpre\u003epython -m venv \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/venv/hrda\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/venv/hrda/bin/activate\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn that environment, the requirements can be installed with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html\npip install mmcv-full==1.3.7 # requires the other packages to be installed first\"\u003e\u003cpre\u003epip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html\npip install mmcv-full==1.3.7 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e requires the other packages to be installed first\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease, download the MiT-B5 ImageNet weights provided by \u003ca href=\"https://github.com/NVlabs/SegFormer?tab=readme-ov-file#training\"\u003eSegFormer\u003c/a\u003e\nfrom their \u003ca href=\"https://connecthkuhk-my.sharepoint.com/:f:/g/personal/xieenze_connect_hku_hk/EvOn3l1WyM5JpnMQFSEO5b8B7vrHw9kDaJGII-3N9KNhrg?e=cpydzZ\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e and put them in the folder \u003ccode\u003epretrained/\u003c/code\u003e.\nFurther, download the checkpoint of \u003ca href=\"https://drive.google.com/file/d/1O6n1HearrXHZTHxNRWp8HCMyqbulKcSW/view?usp=sharing\" rel=\"nofollow\"\u003eHRDA on GTA→Cityscapes\u003c/a\u003e and extract it to the folder \u003ccode\u003ework_dirs/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSetup Datasets\u003c/h2\u003e\u003ca id=\"user-content-setup-datasets\" class=\"anchor\" aria-label=\"Permalink: Setup Datasets\" href=\"#setup-datasets\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCityscapes:\u003c/strong\u003e Please, download leftImg8bit_trainvaltest.zip and\ngt_trainvaltest.zip from \u003ca href=\"https://www.cityscapes-dataset.com/downloads/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\nand extract them to \u003ccode\u003edata/cityscapes\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eGTA:\u003c/strong\u003e Please, download all image and label packages from\n\u003ca href=\"https://download.visinf.tu-darmstadt.de/data/from_games/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and extract\nthem to \u003ccode\u003edata/gta\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSynthia (Optional):\u003c/strong\u003e Please, download SYNTHIA-RAND-CITYSCAPES from\n\u003ca href=\"http://synthia-dataset.net/downloads/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and extract it to \u003ccode\u003edata/synthia\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eACDC (Optional):\u003c/strong\u003e Please, download rgb_anon_trainvaltest.zip and\ngt_trainval.zip from \u003ca href=\"https://acdc.vision.ee.ethz.ch/download\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and\nextract them to \u003ccode\u003edata/acdc\u003c/code\u003e. Further, please restructure the folders from\n\u003ccode\u003econdition/split/sequence/\u003c/code\u003e to \u003ccode\u003esplit/\u003c/code\u003e using the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"rsync -a data/acdc/rgb_anon/*/train/*/* data/acdc/rgb_anon/train/\nrsync -a data/acdc/rgb_anon/*/val/*/* data/acdc/rgb_anon/val/\nrsync -a data/acdc/gt/*/train/*/*_labelTrainIds.png data/acdc/gt/train/\nrsync -a data/acdc/gt/*/val/*/*_labelTrainIds.png data/acdc/gt/val/\"\u003e\u003cpre\u003ersync -a data/acdc/rgb_anon/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/train/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e data/acdc/rgb_anon/train/\nrsync -a data/acdc/rgb_anon/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/val/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e data/acdc/rgb_anon/val/\nrsync -a data/acdc/gt/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/train/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e_labelTrainIds.png data/acdc/gt/train/\nrsync -a data/acdc/gt/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/val/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e/\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e_labelTrainIds.png data/acdc/gt/val/\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDark Zurich (Optional):\u003c/strong\u003e Please, download the Dark_Zurich_train_anon.zip\nand Dark_Zurich_val_anon.zip from\n\u003ca href=\"https://www.trace.ethz.ch/publications/2019/GCMA_UIoU/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and extract it\nto \u003ccode\u003edata/dark_zurich\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBDD100K (Optional):\u003c/strong\u003e Please, download the \u003ccode\u003e10K Images\u003c/code\u003e and \u003ccode\u003eSegmentation\u003c/code\u003e from\n\u003ca href=\"https://bdd-data.berkeley.edu/portal.html#download\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and extract it\nto \u003ccode\u003edata/bdd100k\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMapillary (Optional):\u003c/strong\u003e Please, download the mapillary-vistas-dataset_public_v1.2.zip\nfrom \u003ca href=\"https://www.mapillary.com/dataset/vistas\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and extract it\nto \u003ccode\u003edata/mapillary\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe final folder structure should look like this:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"HRDA\n├── ...\n├── data\n│ ├── acdc (optional)\n│ │ ├── gt\n│ │ │ ├── train\n│ │ │ ├── val\n│ │ ├── rgb_anon\n│ │ │ ├── train\n│ │ │ ├── val\n│ ├── bdd100k (optional)\n│ │ ├── images/10k/val\n│ │ ├── labels/sem_seg/masks/val\n│ ├── cityscapes\n│ │ ├── leftImg8bit\n│ │ │ ├── train\n│ │ │ ├── val\n│ │ ├── gtFine\n│ │ │ ├── train\n│ │ │ ├── val\n│ ├── dark_zurich (optional)\n│ │ ├── gt\n│ │ │ ├── val\n│ │ ├── rgb_anon\n│ │ │ ├── train\n│ │ │ ├── val\n│ ├── gta\n│ │ ├── images\n│ │ ├── labels\n│ ├── mapillary (optional)\n│ │ ├── validation/images\n│ │ ├── validation/labels\n│ ├── synthia (optional)\n│ │ ├── RGB\n│ │ ├── GT\n│ │ │ ├── LABELS\n├── ...\"\u003e\u003cpre lang=\"none\" class=\"notranslate\"\u003e\u003ccode\u003eHRDA\n├── ...\n├── data\n│ ├── acdc (optional)\n│ │ ├── gt\n│ │ │ ├── train\n│ │ │ ├── val\n│ │ ├── rgb_anon\n│ │ │ ├── train\n│ │ │ ├── val\n│ ├── bdd100k (optional)\n│ │ ├── images/10k/val\n│ │ ├── labels/sem_seg/masks/val\n│ ├── cityscapes\n│ │ ├── leftImg8bit\n│ │ │ ├── train\n│ │ │ ├── val\n│ │ ├── gtFine\n│ │ │ ├── train\n│ │ │ ├── val\n│ ├── dark_zurich (optional)\n│ │ ├── gt\n│ │ │ ├── val\n│ │ ├── rgb_anon\n│ │ │ ├── train\n│ │ │ ├── val\n│ ├── gta\n│ │ ├── images\n│ │ ├── labels\n│ ├── mapillary (optional)\n│ │ ├── validation/images\n│ │ ├── validation/labels\n│ ├── synthia (optional)\n│ │ ├── RGB\n│ │ ├── GT\n│ │ │ ├── LABELS\n├── ...\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eData Preprocessing:\u003c/strong\u003e Finally, please run the following scripts to convert the label IDs to the\ntrain IDs and to generate the class index for RCS:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python tools/convert_datasets/gta.py data/gta --nproc 8\npython tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8\npython tools/convert_datasets/synthia.py data/synthia/ --nproc 8\npython tools/convert_datasets/mapillary.py data/mapillary/ --nproc 8\"\u003e\u003cpre\u003epython tools/convert_datasets/gta.py data/gta --nproc 8\npython tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8\npython tools/convert_datasets/synthia.py data/synthia/ --nproc 8\npython tools/convert_datasets/mapillary.py data/mapillary/ --nproc 8\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTesting \u0026amp; Predictions\u003c/h2\u003e\u003ca id=\"user-content-testing--predictions\" class=\"anchor\" aria-label=\"Permalink: Testing \u0026amp; Predictions\" href=\"#testing--predictions\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe provided HRDA checkpoint trained on GTA→Cityscapes can be tested on the\nCityscapes validation set using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"sh test.sh work_dirs/gtaHR2csHR_hrda_246ef\"\u003e\u003cpre\u003esh test.sh work_dirs/gtaHR2csHR_hrda_246ef\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe predictions are saved for inspection to\n\u003ccode\u003ework_dirs/gtaHR2csHR_hrda_246ef/preds\u003c/code\u003e\nand the mIoU of the model is printed to the console. The provided checkpoint\nshould achieve 73.79 mIoU. Refer to the end of\n\u003ccode\u003ework_dirs/gtaHR2csHR_hrda_246ef/20220215_002056.log\u003c/code\u003e for\nmore information such as the class-wise IoU.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you want to visualize the LR predictions, HR predictions, or scale\nattentions of HRDA on the validation set, please refer to \u003ca href=\"/lhoyer/HRDA/blob/master/test.sh\"\u003etest.sh\u003c/a\u003e for\nfurther instructions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining\u003c/h2\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor convenience, we provide an \u003ca href=\"/lhoyer/HRDA/blob/master/configs/hrda/gtaHR2csHR_hrda.py\"\u003eannotated config file\u003c/a\u003e\nof the final HRDA. A training job can be launched using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python run_experiments.py --config configs/hrda/gtaHR2csHR_hrda.py\"\u003e\u003cpre\u003epython run_experiments.py --config configs/hrda/gtaHR2csHR_hrda.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe logs and checkpoints are stored in \u003ccode\u003ework_dirs/\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor the other experiments in our paper, we use a script to automatically\ngenerate and train the configs:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python run_experiments.py --exp \u0026lt;ID\u0026gt;\"\u003e\u003cpre\u003epython run_experiments.py --exp \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eID\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eMore information about the available experiments and their assigned IDs, can be\nfound in \u003ca href=\"/lhoyer/HRDA/blob/master/experiments.py\"\u003eexperiments.py\u003c/a\u003e. The generated configs will be stored\nin \u003ccode\u003econfigs/generated/\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhen evaluating a model trained on Synthia→Cityscapes, please note that the\nevaluation script calculates the mIoU for all 19 Cityscapes classes. However,\nSynthia contains only labels for 16 of these classes. Therefore, it is a common\npractice in UDA to report the mIoU for Synthia→Cityscapes only on these 16\nclasses. As the Iou for the 3 missing classes is 0, you can do the conversion\n\u003ccode\u003emIoU16 = mIoU19 * 19 / 16\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe results for Cityscapes→ACDC and Cityscapes→DarkZurich are reported on\nthe test split of the target dataset. To generate the predictions for the test\nset, please run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m tools.test path/to/config_file path/to/checkpoint_file --test-set --format-only --eval-option imgfile_prefix=labelTrainIds to_label_id=False\"\u003e\u003cpre\u003epython -m tools.test path/to/config_file path/to/checkpoint_file --test-set --format-only --eval-option imgfile_prefix=labelTrainIds to_label_id=False\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe predictions can be submitted to the public evaluation server of the\nrespective dataset to obtain the test score.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDomain Generalization\u003c/h2\u003e\u003ca id=\"user-content-domain-generalization\" class=\"anchor\" aria-label=\"Permalink: Domain Generalization\" href=\"#domain-generalization\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHRDA/DAFormer for domain generalization (DG) is located on the DG branch, which can\nbe checked out with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git checkout dg\"\u003e\u003cpre\u003egit checkout dg\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThey can be trained for DG using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python run_experiments.py --exp 50\"\u003e\u003cpre\u003epython run_experiments.py --exp 50\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor further details, please refer to \u003ca href=\"/lhoyer/HRDA/blob/master/experiments.py\"\u003eexperiment.py\u003c/a\u003e. The model is\ndirectly evaluated on Cityscapes during training with GTA data only. It can be additionally\nevaluated on BDD100K and Mapillary with \u003ca href=\"/lhoyer/HRDA/blob/master/tools/test.py\"\u003etools/test.py\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m tools.test path/to/config_file path/to/checkpoint_file --eval mIoU --dataset BDD100K\npython -m tools.test path/to/config_file path/to/checkpoint_file --eval mIoU --dataset Mapillary --eval-option efficient_test=True\"\u003e\u003cpre\u003epython -m tools.test path/to/config_file path/to/checkpoint_file --eval mIoU --dataset BDD100K\npython -m tools.test path/to/config_file path/to/checkpoint_file --eval mIoU --dataset Mapillary --eval-option efficient_test=True\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCheckpoints\u003c/h2\u003e\u003ca id=\"user-content-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Checkpoints\" href=\"#checkpoints\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBelow, we provide checkpoints of HRDA for different benchmarks.\nThey come together with the log files of their training.\nAs the results in the paper are provided as the mean over three random\nseeds, we provide the checkpoint with the median validation performance here.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://drive.google.com/file/d/1O6n1HearrXHZTHxNRWp8HCMyqbulKcSW/view?usp=sharing\" rel=\"nofollow\"\u003eHRDA for GTA→Cityscapes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://drive.google.com/file/d/1V6kSLq3FH8o-FJf-syiiQx6E74ML9Huw/view?usp=sharing\" rel=\"nofollow\"\u003eHRDA for Synthia→Cityscapes\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://drive.google.com/file/d/1Pwb1NAKx-WeVdhKEfafn29_JP4iEyKQM/view?usp=sharing\" rel=\"nofollow\"\u003eHRDA for Cityscapes→ACDC\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://drive.google.com/file/d/1QEfolITfZWA_gvtTtVgv8oa-exP9PEo0/view?usp=sharing\" rel=\"nofollow\"\u003eHRDA for Cityscapes→DarkZurich\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://drive.google.com/file/d/19MA5NJTFJrbwAdgYeqjfSaPCx9Fnmm4M/view?usp=sharing\" rel=\"nofollow\"\u003eHRDA for GTA Domain Generalization\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://drive.google.com/file/d/1up9x3R3HtU_MjM6F89xNIHzPbIqBSacx/view?usp=sharing\" rel=\"nofollow\"\u003eDAFormer for GTA Domain Generalization\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe checkpoints come with the training logs. Please note that:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe logs provide the mIoU for 19 classes. For Synthia→Cityscapes, it is\nnecessary to convert the mIoU to the 16 valid classes. Please, read the\nsection above for converting the mIoU.\u003c/li\u003e\n\u003cli\u003eThe logs provide the mIoU on the validation set. For Cityscapes→ACDC and\nCityscapes→DarkZurich the results reported in the paper are calculated on the\ntest split. For DarkZurich, the performance significantly differs between\nvalidation and test split. Please, read the section above on how to obtain\nthe test mIoU.\u003c/li\u003e\n\u003cli\u003eThe logs for domain generalization (DG) provide the validation performance on Cityscapes.\nPlease, refer to the section above to evaluate the checkpoint on BDD100K and Mapillary.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFramework Structure\u003c/h2\u003e\u003ca id=\"user-content-framework-structure\" class=\"anchor\" aria-label=\"Permalink: Framework Structure\" href=\"#framework-structure\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis project is based on \u003ca href=\"https://github.com/open-mmlab/mmsegmentation/tree/v0.16.0\"\u003emmsegmentation version 0.16.0\u003c/a\u003e.\nFor more information about the framework structure and the config system,\nplease refer to the \u003ca href=\"https://mmsegmentation.readthedocs.io/en/latest/index.html\" rel=\"nofollow\"\u003emmsegmentation documentation\u003c/a\u003e\nand the \u003ca href=\"https://mmcv.readthedocs.ihttps://arxiv.org/abs/2007.08702o/en/v1.3.7/index.html\" rel=\"nofollow\"\u003emmcv documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe most relevant files for HRDA are:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/lhoyer/HRDA/blob/master/configs/hrda/gtaHR2csHR_hrda.py\"\u003econfigs/hrda/gtaHR2csHR_hrda.py\u003c/a\u003e:\nAnnotated config file for the final HRDA.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/lhoyer/HRDA/blob/master/mmseg/models/segmentors/hrda_encoder_decoder.py\"\u003emmseg/models/segmentors/hrda_encoder_decoder.py\u003c/a\u003e:\nImplementation of the HRDA multi-resolution encoding with context and detail crop.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/lhoyer/HRDA/blob/master/mmseg/models/decode_heads/hrda_head.py\"\u003emmseg/models/decode_heads/hrda_head.py\u003c/a\u003e:\nImplementation of the HRDA decoding with multi-resolution fusion and scale attention.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/lhoyer/HRDA/blob/master/mmseg/models/uda/dacs.py\"\u003emmseg/models/uda/dacs.py\u003c/a\u003e:\nImplementation of the DAFormer self-training.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHRDA is based on the following open-source projects. We thank their\nauthors for making the source code publicly available.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lhoyer/DAFormer\"\u003eDAFormer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation\"\u003eMMSegmentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVlabs/SegFormer\"\u003eSegFormer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/vikolss/DACS\"\u003eDACS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/HeliosZhao/SHADE\"\u003eSHADE\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis project is released under the \u003ca href=\"/lhoyer/HRDA/blob/master/LICENSE\"\u003eApache License 2.0\u003c/a\u003e, while some\nspecific features in this repository are with other licenses. Please refer to\n\u003ca href=\"/lhoyer/HRDA/blob/master/LICENSES.md\"\u003eLICENSES.md\u003c/a\u003e for the careful check, if you are using our code for\ncommercial matters.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation","anchor":"hrda-context-aware-high-resolution-domain-adaptive-semantic-segmentation","htmlText":"HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation"},{"level":2,"text":"Overview","anchor":"overview","htmlText":"Overview"},{"level":2,"text":"Comparison with SOTA UDA","anchor":"comparison-with-sota-uda","htmlText":"Comparison with SOTA UDA"},{"level":2,"text":"Comparison with SOTA Domain Generalization (DG)","anchor":"comparison-with-sota-domain-generalization-dg","htmlText":"Comparison with SOTA Domain Generalization (DG)"},{"level":2,"text":"Setup Environment","anchor":"setup-environment","htmlText":"Setup Environment"},{"level":2,"text":"Setup Datasets","anchor":"setup-datasets","htmlText":"Setup Datasets"},{"level":2,"text":"Testing \u0026 Predictions","anchor":"testing--predictions","htmlText":"Testing \u0026amp; Predictions"},{"level":2,"text":"Training","anchor":"training","htmlText":"Training"},{"level":2,"text":"Domain Generalization","anchor":"domain-generalization","htmlText":"Domain Generalization"},{"level":2,"text":"Checkpoints","anchor":"checkpoints","htmlText":"Checkpoints"},{"level":2,"text":"Framework Structure","anchor":"framework-structure","htmlText":"Framework Structure"},{"level":2,"text":"Acknowledgements","anchor":"acknowledgements","htmlText":"Acknowledgements"},{"level":2,"text":"License","anchor":"license","htmlText":"License"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Flhoyer%2FHRDA"}},{"displayName":"LICENSE","repoName":"HRDA","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Flhoyer%2FHRDA"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_smell_icebreaker_ux":true,"accessible_code_button":true,"github_models_repo_tab":false}}}}</script> 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fgColor-default" data-loading="false" data-size="small" data-variant="invisible" aria-describedby=":Raqj8pab:-loading-announcement"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x"><span class="fgColor-default">20 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0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation</h2><a id="user-content-hrda-context-aware-high-resolution-domain-adaptive-semantic-segmentation" class="anchor" aria-label="Permalink: HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation" href="#hrda-context-aware-high-resolution-domain-adaptive-semantic-segmentation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>by <a href="https://lhoyer.github.io/" rel="nofollow">Lukas Hoyer</a>, <a href="https://vas.mpi-inf.mpg.de/dengxin/" rel="nofollow">Dengxin Dai</a>, and <a href="https://scholar.google.de/citations?user=TwMib_QAAAAJ&hl=en" rel="nofollow">Luc Van Gool</a></strong></p> <p dir="auto"><strong><a href="https://arxiv.org/pdf/2204.13132" rel="nofollow">[ECCV22 Paper]</a></strong> <strong><a href="https://arxiv.org/pdf/2304.13615.pdf" rel="nofollow">[Extension Paper]</a></strong></p> <p dir="auto">🔔 <strong>News:</strong></p> <ul dir="auto"> <li>[2024-07-03] We are happy to announce that our work <a href="https://github.com/google-research/semivl">SemiVL</a> on semi-supervised semantic segmentation with vision-language guidance was accepted at <strong>ECCV24</strong>.</li> <li>[2024-07-03] We are happy to announce that our follow-up work <a href="https://dginstyle.github.io/" rel="nofollow">DGInStyle</a> on image diffusion for domain-generalizable semantic segmentation was accepted at <strong>ECCV24</strong>.</li> <li>[2023-09-26] We are happy to announce that our <a href="https://arxiv.org/pdf/2304.13615.pdf" rel="nofollow">Extension Paper</a> on domain generalization and clear-to-adverse-weather UDA was accapted at <strong>PAMI</strong>.</li> <li>[2023-08-25] We are happy to announce that our follow-up work <a href="https://github.com/susaha/edaps">EDAPS</a> on panoptic segmentation UDA was accepted at <strong>ICCV23</strong>.</li> <li>[2023-04-27] We further extend HRDA to domain generalization and clear-to-adverse-weather UDA in the <a href="https://arxiv.org/pdf/2304.13615.pdf" rel="nofollow">Extension Paper</a>.</li> <li>[2023-02-28] We are happy to announce that our follow-up work <a href="https://github.com/lhoyer/MIC">MIC</a> on context-enhanced UDA was accepted at <strong>CVPR23</strong>.</li> <li>[2022-07-05] We are happy to announce that HRDA was accepted at <strong>ECCV22</strong>.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Overview</h2><a id="user-content-overview" class="anchor" aria-label="Permalink: Overview" href="#overview"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Unsupervised domain adaptation (UDA)</strong> aims to adapt a model trained on synthetic data to real-world data without requiring expensive annotations of real-world images. As UDA methods for semantic segmentation are usually GPU memory intensive, most <strong>previous methods operate only on downscaled images</strong>. We question this design as low-resolution predictions often fail to preserve fine details. The alternative of training with random crops of high-resolution images alleviates this problem but falls short in capturing long-range, domain-robust context information.</p> <p dir="auto">Therefore, we propose <strong>HRDA</strong>, a multi-resolution training approach for UDA, that combines the strengths of small high-resolution crops to <strong>preserve fine segmentation details</strong> and large low-resolution crops to <strong>capture long-range context dependencies</strong> with a learned scale attention, while maintaining a <strong>manageable GPU memory</strong> footprint.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/lhoyer/HRDA/blob/master/resources/hrda_overview.png"><img src="/lhoyer/HRDA/raw/master/resources/hrda_overview.png" alt="HRDA Overview" style="max-width: 100%;"></a></p> <p dir="auto">HRDA enables adapting small objects and preserving fine segmentation details. It significantly improves the state-of-the-art performance <strong>by 5.5 mIoU for GTA→Cityscapes</strong> and <strong>by 4.9 mIoU for Synthia→Cityscapes</strong>, resulting in an unprecedented performance of 73.8 and 65.8 mIoU, respectively.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/lhoyer/HRDA/blob/master/resources/uda_over_time_hrda.png"><img src="/lhoyer/HRDA/raw/master/resources/uda_over_time_hrda.png" alt="UDA over time" style="max-width: 100%;"></a></p> <p dir="auto">The more detailed domain-adaptive semantic segmentation of HRDA, compared to the previous state-of-the-art UDA method DAFormer, can also be observed in example predictions from the Cityscapes validation set.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/lhoyer/HRDA/blob/master/resources/demo.gif"><img src="/lhoyer/HRDA/raw/master/resources/demo.gif" alt="Demo" data-animated-image="" style="max-width: 100%;"></a></p> <details open="" class="details-reset border rounded-2"> <summary class="px-3 py-2"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-device-camera-video"> <path d="M16 3.75v8.5a.75.75 0 0 1-1.136.643L11 10.575v.675A1.75 1.75 0 0 1 9.25 13h-7.5A1.75 1.75 0 0 1 0 11.25v-6.5C0 3.784.784 3 1.75 3h7.5c.966 0 1.75.784 1.75 1.75v.675l3.864-2.318A.75.75 0 0 1 16 3.75Zm-6.5 1a.25.25 0 0 0-.25-.25h-7.5a.25.25 0 0 0-.25.25v6.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-6.5ZM11 8.825l3.5 2.1v-5.85l-3.5 2.1Z"></path> </svg> <span aria-label="Video description HRDA.Slider.Demo.mp4" class="m-1">HRDA.Slider.Demo.mp4</span> <span class="dropdown-caret"></span> </summary> <video src="https://private-user-images.githubusercontent.com/1277888/181128057-27b8039f-a4c9-4f6d-9aa8-9b7f364d8921.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.CZ2dbzfFeKQHv302bt6zelcf6y7VF8NrZosF-23VvCo" data-canonical-src="https://private-user-images.githubusercontent.com/1277888/181128057-27b8039f-a4c9-4f6d-9aa8-9b7f364d8921.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.CZ2dbzfFeKQHv302bt6zelcf6y7VF8NrZosF-23VvCo" controls="controls" muted="muted" class="d-block rounded-bottom-2 border-top width-fit" style="max-height:640px; min-height: 200px"> </video> </details> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/lhoyer/HRDA/blob/master/resources/color_palette.png"><img src="/lhoyer/HRDA/raw/master/resources/color_palette.png" alt="Color Palette" style="max-width: 100%;"></a></p> <p dir="auto">HRDA can be further <strong>extended to domain generalization</strong> lifting the requirement of access to target images. Also in domain generalization, HRDA significantly improves the state-of-the-art performance by <strong>+4.2 mIoU</strong>.</p> <p dir="auto">For more information on HRDA, please check our <a href="https://arxiv.org/pdf/2204.13132" rel="nofollow">[ECCV Paper]</a> and the <a href="https://arxiv.org/pdf/2304.13615.pdf" rel="nofollow">[Extension Paper]</a>.</p> <p dir="auto">If you find HRDA useful in your research, please consider citing:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@InProceedings{hoyer2022hrda, title={{HRDA}: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation}, author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc}, booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, pages={372--391}, year={2022} } @Article{hoyer2024domain, title={Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation}, author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)}, year={2024}, volume={46}, number={1}, pages={220-235}, doi={10.1109/TPAMI.2023.3320613} }"><pre class="notranslate"><code>@InProceedings{hoyer2022hrda, title={{HRDA}: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation}, author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc}, booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, pages={372--391}, year={2022} } @Article{hoyer2024domain, title={Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation}, author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)}, year={2024}, volume={46}, number={1}, pages={220-235}, doi={10.1109/TPAMI.2023.3320613} } </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Comparison with SOTA UDA</h2><a id="user-content-comparison-with-sota-uda" class="anchor" aria-label="Permalink: Comparison with SOTA UDA" href="#comparison-with-sota-uda"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">HRDA significantly outperforms previous works on several UDA benchmarks. This includes synthetic-to-real adaptation on GTA→Cityscapes and Synthia→Cityscapes as well as clear-to-adverse-weather adaptation on Cityscapes→ACDC and Cityscapes→DarkZurich.</p> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th>GTA→CS(val)</th> <th>Synthia→CS(val)</th> <th>CS→ACDC(test)</th> <th>CS→DarkZurich(test)</th> </tr> </thead> <tbody> <tr> <td>ADVENT [1]</td> <td>45.5</td> <td>41.2</td> <td>32.7</td> <td>29.7</td> </tr> <tr> <td>BDL [2]</td> <td>48.5</td> <td>--</td> <td>37.7</td> <td>30.8</td> </tr> <tr> <td>FDA [3]</td> <td>50.5</td> <td>--</td> <td>45.7</td> <td>--</td> </tr> <tr> <td>DACS [4]</td> <td>52.1</td> <td>48.3</td> <td>--</td> <td>--</td> </tr> <tr> <td>ProDA [5]</td> <td>57.5</td> <td>55.5</td> <td>--</td> <td>--</td> </tr> <tr> <td>MGCDA [6]</td> <td>--</td> <td>--</td> <td>48.7</td> <td>42.5</td> </tr> <tr> <td>DANNet [7]</td> <td>--</td> <td>--</td> <td>50.0</td> <td>45.2</td> </tr> <tr> <td>DAFormer (Ours) [8]</td> <td>68.3</td> <td>60.9</td> <td>55.4*</td> <td>53.8*</td> </tr> <tr> <td>HRDA (Ours)</td> <td><strong>73.8</strong></td> <td><strong>65.8</strong></td> <td><strong>68.0</strong>*</td> <td><strong>55.9</strong>*</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">* New results of our <a href="https://arxiv.org/pdf/2304.13615.pdf" rel="nofollow">extension paper</a></p> <p dir="auto">References:</p> <ol dir="auto"> <li>Vu et al. "Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation" in CVPR 2019.</li> <li>Li et al. "Bidirectional learning for domain adaptation of semantic segmentation" in CVPR 2019.</li> <li>Yang et al. "Fda: Fourier domain adaptation for semantic segmentation" in CVPR 2020.</li> <li>Tranheden et al. "Dacs: Domain adaptation via crossdomain mixed sampling" in WACV 2021.</li> <li>Zhang et al. "Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation" in CVPR 2021.</li> <li>Sakaridis et al. "Map-guided curriculum domain adaptation and uncertaintyaware evaluation for semantic nighttime image segmentation" in TPAMI, 2020.</li> <li>Wu et al. "DANNet: A one-stage domain adaptation network for unsupervised nighttime semantic segmentation" in CVPR, 2021.</li> <li>Hoyer et al. "DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation" in CVPR, 2022.</li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Comparison with SOTA Domain Generalization (DG)</h2><a id="user-content-comparison-with-sota-domain-generalization-dg" class="anchor" aria-label="Permalink: Comparison with SOTA Domain Generalization (DG)" href="#comparison-with-sota-domain-generalization-dg"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">HRDA and DAFormer significantly outperform previous works on domain generalization from GTA to real street scenes.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>DG Method</th> <th>Cityscapes</th> <th>BDD100K</th> <th>Mapillary</th> <th>Avg.</th> </tr> </thead> <tbody> <tr> <td>IBN-Net [1,5]</td> <td>37.37</td> <td>34.21</td> <td>36.81</td> <td>36.13</td> </tr> <tr> <td>DRPC [2]</td> <td>42.53</td> <td>38.72</td> <td>38.05</td> <td>39.77</td> </tr> <tr> <td>ISW [3,5]</td> <td>37.20</td> <td>33.36</td> <td>35.57</td> <td>35.38</td> </tr> <tr> <td>SAN-SAW [4]</td> <td>45.33</td> <td>41.18</td> <td>40.77</td> <td>42.43</td> </tr> <tr> <td>SHADE [5]</td> <td>46.66</td> <td>43.66</td> <td>45.50</td> <td>45.27</td> </tr> <tr> <td>DAFormer (Ours) [6]</td> <td>52.65*</td> <td>47.89*</td> <td>54.66*</td> <td>51.73*</td> </tr> <tr> <td>HRDA (Ours)</td> <td><strong>57.41*</strong></td> <td><strong>49.11*</strong></td> <td><strong>61.16*</strong></td> <td><strong>55.90*</strong></td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">* New results of our <a href="https://arxiv.org/pdf/2304.13615.pdf" rel="nofollow">extension paper</a></p> <p dir="auto">References:</p> <ol dir="auto"> <li>Pan et al. "Two at once: Enhancing learning and generalization capacities via IBN-Net" in ECCV, 2018.</li> <li>Yue et al. "Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data" ICCV, 2019.</li> <li>Choi et al. "RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening" in CVPR, 2021.</li> <li>Peng et al. "Semantic-aware domain generalized segmentation" in CVPR, 2022.</li> <li>Zhao et al. "Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation" in ECCV, 2022.</li> <li>Hoyer et al. "DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation" in CVPR, 2022.</li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Setup Environment</h2><a id="user-content-setup-environment" class="anchor" aria-label="Permalink: Setup Environment" href="#setup-environment"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For this project, we used python 3.8.5. We recommend setting up a new virtual environment:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m venv ~/venv/hrda source ~/venv/hrda/bin/activate"><pre>python -m venv <span class="pl-k">~</span>/venv/hrda <span class="pl-c1">source</span> <span class="pl-k">~</span>/venv/hrda/bin/activate</pre></div> <p dir="auto">In that environment, the requirements can be installed with:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html pip install mmcv-full==1.3.7 # requires the other packages to be installed first"><pre>pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html pip install mmcv-full==1.3.7 <span class="pl-c"><span class="pl-c">#</span> requires the other packages to be installed first</span></pre></div> <p dir="auto">Please, download the MiT-B5 ImageNet weights provided by <a href="https://github.com/NVlabs/SegFormer?tab=readme-ov-file#training">SegFormer</a> from their <a href="https://connecthkuhk-my.sharepoint.com/:f:/g/personal/xieenze_connect_hku_hk/EvOn3l1WyM5JpnMQFSEO5b8B7vrHw9kDaJGII-3N9KNhrg?e=cpydzZ" rel="nofollow">OneDrive</a> and put them in the folder <code>pretrained/</code>. Further, download the checkpoint of <a href="https://drive.google.com/file/d/1O6n1HearrXHZTHxNRWp8HCMyqbulKcSW/view?usp=sharing" rel="nofollow">HRDA on GTA→Cityscapes</a> and extract it to the folder <code>work_dirs/</code>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Setup Datasets</h2><a id="user-content-setup-datasets" class="anchor" aria-label="Permalink: Setup Datasets" href="#setup-datasets"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Cityscapes:</strong> Please, download leftImg8bit_trainvaltest.zip and gt_trainvaltest.zip from <a href="https://www.cityscapes-dataset.com/downloads/" rel="nofollow">here</a> and extract them to <code>data/cityscapes</code>.</p> <p dir="auto"><strong>GTA:</strong> Please, download all image and label packages from <a href="https://download.visinf.tu-darmstadt.de/data/from_games/" rel="nofollow">here</a> and extract them to <code>data/gta</code>.</p> <p dir="auto"><strong>Synthia (Optional):</strong> Please, download SYNTHIA-RAND-CITYSCAPES from <a href="http://synthia-dataset.net/downloads/" rel="nofollow">here</a> and extract it to <code>data/synthia</code>.</p> <p dir="auto"><strong>ACDC (Optional):</strong> Please, download rgb_anon_trainvaltest.zip and gt_trainval.zip from <a href="https://acdc.vision.ee.ethz.ch/download" rel="nofollow">here</a> and extract them to <code>data/acdc</code>. Further, please restructure the folders from <code>condition/split/sequence/</code> to <code>split/</code> using the following commands:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="rsync -a data/acdc/rgb_anon/*/train/*/* data/acdc/rgb_anon/train/ rsync -a data/acdc/rgb_anon/*/val/*/* data/acdc/rgb_anon/val/ rsync -a data/acdc/gt/*/train/*/*_labelTrainIds.png data/acdc/gt/train/ rsync -a data/acdc/gt/*/val/*/*_labelTrainIds.png data/acdc/gt/val/"><pre>rsync -a data/acdc/rgb_anon/<span class="pl-k">*</span>/train/<span class="pl-k">*</span>/<span class="pl-k">*</span> data/acdc/rgb_anon/train/ rsync -a data/acdc/rgb_anon/<span class="pl-k">*</span>/val/<span class="pl-k">*</span>/<span class="pl-k">*</span> data/acdc/rgb_anon/val/ rsync -a data/acdc/gt/<span class="pl-k">*</span>/train/<span class="pl-k">*</span>/<span class="pl-k">*</span>_labelTrainIds.png data/acdc/gt/train/ rsync -a data/acdc/gt/<span class="pl-k">*</span>/val/<span class="pl-k">*</span>/<span class="pl-k">*</span>_labelTrainIds.png data/acdc/gt/val/</pre></div> <p dir="auto"><strong>Dark Zurich (Optional):</strong> Please, download the Dark_Zurich_train_anon.zip and Dark_Zurich_val_anon.zip from <a href="https://www.trace.ethz.ch/publications/2019/GCMA_UIoU/" rel="nofollow">here</a> and extract it to <code>data/dark_zurich</code>.</p> <p dir="auto"><strong>BDD100K (Optional):</strong> Please, download the <code>10K Images</code> and <code>Segmentation</code> from <a href="https://bdd-data.berkeley.edu/portal.html#download" rel="nofollow">here</a> and extract it to <code>data/bdd100k</code>.</p> <p dir="auto"><strong>Mapillary (Optional):</strong> Please, download the mapillary-vistas-dataset_public_v1.2.zip from <a href="https://www.mapillary.com/dataset/vistas" rel="nofollow">here</a> and extract it to <code>data/mapillary</code>.</p> <p dir="auto">The final folder structure should look like this:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="HRDA ├── ... ├── data │ ├── acdc (optional) │ │ ├── gt │ │ │ ├── train │ │ │ ├── val │ │ ├── rgb_anon │ │ │ ├── train │ │ │ ├── val │ ├── bdd100k (optional) │ │ ├── images/10k/val │ │ ├── labels/sem_seg/masks/val │ ├── cityscapes │ │ ├── leftImg8bit │ │ │ ├── train │ │ │ ├── val │ │ ├── gtFine │ │ │ ├── train │ │ │ ├── val │ ├── dark_zurich (optional) │ │ ├── gt │ │ │ ├── val │ │ ├── rgb_anon │ │ │ ├── train │ │ │ ├── val │ ├── gta │ │ ├── images │ │ ├── labels │ ├── mapillary (optional) │ │ ├── validation/images │ │ ├── validation/labels │ ├── synthia (optional) │ │ ├── RGB │ │ ├── GT │ │ │ ├── LABELS ├── ..."><pre lang="none" class="notranslate"><code>HRDA ├── ... ├── data │ ├── acdc (optional) │ │ ├── gt │ │ │ ├── train │ │ │ ├── val │ │ ├── rgb_anon │ │ │ ├── train │ │ │ ├── val │ ├── bdd100k (optional) │ │ ├── images/10k/val │ │ ├── labels/sem_seg/masks/val │ ├── cityscapes │ │ ├── leftImg8bit │ │ │ ├── train │ │ │ ├── val │ │ ├── gtFine │ │ │ ├── train │ │ │ ├── val │ ├── dark_zurich (optional) │ │ ├── gt │ │ │ ├── val │ │ ├── rgb_anon │ │ │ ├── train │ │ │ ├── val │ ├── gta │ │ ├── images │ │ ├── labels │ ├── mapillary (optional) │ │ ├── validation/images │ │ ├── validation/labels │ ├── synthia (optional) │ │ ├── RGB │ │ ├── GT │ │ │ ├── LABELS ├── ... </code></pre></div> <p dir="auto"><strong>Data Preprocessing:</strong> Finally, please run the following scripts to convert the label IDs to the train IDs and to generate the class index for RCS:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python tools/convert_datasets/gta.py data/gta --nproc 8 python tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8 python tools/convert_datasets/synthia.py data/synthia/ --nproc 8 python tools/convert_datasets/mapillary.py data/mapillary/ --nproc 8"><pre>python tools/convert_datasets/gta.py data/gta --nproc 8 python tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8 python tools/convert_datasets/synthia.py data/synthia/ --nproc 8 python tools/convert_datasets/mapillary.py data/mapillary/ --nproc 8</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Testing & Predictions</h2><a id="user-content-testing--predictions" class="anchor" aria-label="Permalink: Testing & Predictions" href="#testing--predictions"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The provided HRDA checkpoint trained on GTA→Cityscapes can be tested on the Cityscapes validation set using:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="sh test.sh work_dirs/gtaHR2csHR_hrda_246ef"><pre>sh test.sh work_dirs/gtaHR2csHR_hrda_246ef</pre></div> <p dir="auto">The predictions are saved for inspection to <code>work_dirs/gtaHR2csHR_hrda_246ef/preds</code> and the mIoU of the model is printed to the console. The provided checkpoint should achieve 73.79 mIoU. Refer to the end of <code>work_dirs/gtaHR2csHR_hrda_246ef/20220215_002056.log</code> for more information such as the class-wise IoU.</p> <p dir="auto">If you want to visualize the LR predictions, HR predictions, or scale attentions of HRDA on the validation set, please refer to <a href="/lhoyer/HRDA/blob/master/test.sh">test.sh</a> for further instructions.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Training</h2><a id="user-content-training" class="anchor" aria-label="Permalink: Training" href="#training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For convenience, we provide an <a href="/lhoyer/HRDA/blob/master/configs/hrda/gtaHR2csHR_hrda.py">annotated config file</a> of the final HRDA. A training job can be launched using:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python run_experiments.py --config configs/hrda/gtaHR2csHR_hrda.py"><pre>python run_experiments.py --config configs/hrda/gtaHR2csHR_hrda.py</pre></div> <p dir="auto">The logs and checkpoints are stored in <code>work_dirs/</code>.</p> <p dir="auto">For the other experiments in our paper, we use a script to automatically generate and train the configs:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python run_experiments.py --exp <ID>"><pre>python run_experiments.py --exp <span class="pl-k"><</span>ID<span class="pl-k">></span></pre></div> <p dir="auto">More information about the available experiments and their assigned IDs, can be found in <a href="/lhoyer/HRDA/blob/master/experiments.py">experiments.py</a>. The generated configs will be stored in <code>configs/generated/</code>.</p> <p dir="auto">When evaluating a model trained on Synthia→Cityscapes, please note that the evaluation script calculates the mIoU for all 19 Cityscapes classes. However, Synthia contains only labels for 16 of these classes. Therefore, it is a common practice in UDA to report the mIoU for Synthia→Cityscapes only on these 16 classes. As the Iou for the 3 missing classes is 0, you can do the conversion <code>mIoU16 = mIoU19 * 19 / 16</code>.</p> <p dir="auto">The results for Cityscapes→ACDC and Cityscapes→DarkZurich are reported on the test split of the target dataset. To generate the predictions for the test set, please run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m tools.test path/to/config_file path/to/checkpoint_file --test-set --format-only --eval-option imgfile_prefix=labelTrainIds to_label_id=False"><pre>python -m tools.test path/to/config_file path/to/checkpoint_file --test-set --format-only --eval-option imgfile_prefix=labelTrainIds to_label_id=False</pre></div> <p dir="auto">The predictions can be submitted to the public evaluation server of the respective dataset to obtain the test score.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Domain Generalization</h2><a id="user-content-domain-generalization" class="anchor" aria-label="Permalink: Domain Generalization" href="#domain-generalization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">HRDA/DAFormer for domain generalization (DG) is located on the DG branch, which can be checked out with:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git checkout dg"><pre>git checkout dg</pre></div> <p dir="auto">They can be trained for DG using:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python run_experiments.py --exp 50"><pre>python run_experiments.py --exp 50</pre></div> <p dir="auto">For further details, please refer to <a href="/lhoyer/HRDA/blob/master/experiments.py">experiment.py</a>. The model is directly evaluated on Cityscapes during training with GTA data only. It can be additionally evaluated on BDD100K and Mapillary with <a href="/lhoyer/HRDA/blob/master/tools/test.py">tools/test.py</a>:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m tools.test path/to/config_file path/to/checkpoint_file --eval mIoU --dataset BDD100K python -m tools.test path/to/config_file path/to/checkpoint_file --eval mIoU --dataset Mapillary --eval-option efficient_test=True"><pre>python -m tools.test path/to/config_file path/to/checkpoint_file --eval mIoU --dataset BDD100K python -m tools.test path/to/config_file path/to/checkpoint_file --eval mIoU --dataset Mapillary --eval-option efficient_test=True</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Checkpoints</h2><a id="user-content-checkpoints" class="anchor" aria-label="Permalink: Checkpoints" href="#checkpoints"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Below, we provide checkpoints of HRDA for different benchmarks. They come together with the log files of their training. As the results in the paper are provided as the mean over three random seeds, we provide the checkpoint with the median validation performance here.</p> <ul dir="auto"> <li><a href="https://drive.google.com/file/d/1O6n1HearrXHZTHxNRWp8HCMyqbulKcSW/view?usp=sharing" rel="nofollow">HRDA for GTA→Cityscapes</a></li> <li><a href="https://drive.google.com/file/d/1V6kSLq3FH8o-FJf-syiiQx6E74ML9Huw/view?usp=sharing" rel="nofollow">HRDA for Synthia→Cityscapes</a></li> <li><a href="https://drive.google.com/file/d/1Pwb1NAKx-WeVdhKEfafn29_JP4iEyKQM/view?usp=sharing" rel="nofollow">HRDA for Cityscapes→ACDC</a></li> <li><a href="https://drive.google.com/file/d/1QEfolITfZWA_gvtTtVgv8oa-exP9PEo0/view?usp=sharing" rel="nofollow">HRDA for Cityscapes→DarkZurich</a></li> <li><a href="https://drive.google.com/file/d/19MA5NJTFJrbwAdgYeqjfSaPCx9Fnmm4M/view?usp=sharing" rel="nofollow">HRDA for GTA Domain Generalization</a></li> <li><a href="https://drive.google.com/file/d/1up9x3R3HtU_MjM6F89xNIHzPbIqBSacx/view?usp=sharing" rel="nofollow">DAFormer for GTA Domain Generalization</a></li> </ul> <p dir="auto">The checkpoints come with the training logs. Please note that:</p> <ul dir="auto"> <li>The logs provide the mIoU for 19 classes. For Synthia→Cityscapes, it is necessary to convert the mIoU to the 16 valid classes. Please, read the section above for converting the mIoU.</li> <li>The logs provide the mIoU on the validation set. For Cityscapes→ACDC and Cityscapes→DarkZurich the results reported in the paper are calculated on the test split. For DarkZurich, the performance significantly differs between validation and test split. Please, read the section above on how to obtain the test mIoU.</li> <li>The logs for domain generalization (DG) provide the validation performance on Cityscapes. Please, refer to the section above to evaluate the checkpoint on BDD100K and Mapillary.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Framework Structure</h2><a id="user-content-framework-structure" class="anchor" aria-label="Permalink: Framework Structure" href="#framework-structure"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This project is based on <a href="https://github.com/open-mmlab/mmsegmentation/tree/v0.16.0">mmsegmentation version 0.16.0</a>. For more information about the framework structure and the config system, please refer to the <a href="https://mmsegmentation.readthedocs.io/en/latest/index.html" rel="nofollow">mmsegmentation documentation</a> and the <a href="https://mmcv.readthedocs.ihttps://arxiv.org/abs/2007.08702o/en/v1.3.7/index.html" rel="nofollow">mmcv documentation</a>.</p> <p dir="auto">The most relevant files for HRDA are:</p> <ul dir="auto"> <li><a href="/lhoyer/HRDA/blob/master/configs/hrda/gtaHR2csHR_hrda.py">configs/hrda/gtaHR2csHR_hrda.py</a>: Annotated config file for the final HRDA.</li> <li><a href="/lhoyer/HRDA/blob/master/mmseg/models/segmentors/hrda_encoder_decoder.py">mmseg/models/segmentors/hrda_encoder_decoder.py</a>: Implementation of the HRDA multi-resolution encoding with context and detail crop.</li> <li><a href="/lhoyer/HRDA/blob/master/mmseg/models/decode_heads/hrda_head.py">mmseg/models/decode_heads/hrda_head.py</a>: Implementation of the HRDA decoding with multi-resolution fusion and scale attention.</li> <li><a href="/lhoyer/HRDA/blob/master/mmseg/models/uda/dacs.py">mmseg/models/uda/dacs.py</a>: Implementation of the DAFormer self-training.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgements</h2><a id="user-content-acknowledgements" class="anchor" aria-label="Permalink: Acknowledgements" href="#acknowledgements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">HRDA is based on the following open-source projects. We thank their authors for making the source code publicly available.</p> <ul dir="auto"> <li><a href="https://github.com/lhoyer/DAFormer">DAFormer</a></li> <li><a href="https://github.com/open-mmlab/mmsegmentation">MMSegmentation</a></li> <li><a href="https://github.com/NVlabs/SegFormer">SegFormer</a></li> <li><a href="https://github.com/vikolss/DACS">DACS</a></li> <li><a href="https://github.com/HeliosZhao/SHADE">SHADE</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">License</h2><a id="user-content-license" class="anchor" aria-label="Permalink: License" href="#license"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This project is released under the <a href="/lhoyer/HRDA/blob/master/LICENSE">Apache License 2.0</a>, while some specific features in this repository are with other licenses. Please refer to <a href="/lhoyer/HRDA/blob/master/LICENSES.md">LICENSES.md</a> for the careful check, if you are using our code for commercial matters.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="GNRUD18H+QjN9I88NvSj2t7SZZFGc3FblKvQ6f+Msp0YulfThLkb7/WgBTIrqRbhP4iiSyNG7ED8nmBkf6OxQA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> [ECCV22] Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/transformer" title="Topic: transformer" data-view-component="true" 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