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GitHub - justinkay/aldi: Official implementation of "Align and Distill: Unifying and Improving Domain Adaptive Object Detection" (TMLR 2025)
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17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 12 1Z"></path> </svg> </a> <div class="flex-1 flex-order-2 text-right"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2Fjustinkay%2Faldi" class="HeaderMenu-link HeaderMenu-button d-inline-flex d-lg-none flex-order-1 f5 no-underline border color-border-default rounded-2 px-2 py-1 color-fg-inherit js-prevent-focus-on-mobile-nav" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/justinkay/aldi","user_id":null}}" data-hydro-click-hmac="8f67ba99a805217f59eeee185aa1c48b1570c095b369587ac313fdd490e1788f" data-analytics-event="{"category":"Marketing nav","action":"click to Sign in","label":"ref_page:Marketing;ref_cta:Sign in;ref_loc:Header"}" > Sign in </a> </div> </div> <div class="HeaderMenu js-header-menu height-fit position-lg-relative d-lg-flex flex-column flex-auto top-0"> <div class="HeaderMenu-wrapper d-flex flex-column flex-self-start flex-lg-row flex-auto rounded rounded-lg-0"> <nav 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 flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <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 pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <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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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" 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</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":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any 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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> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </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":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 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1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </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":"discussions","context":"product","tag":"link","label":"discussions_link_product_navbar"}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Discussions</div> Collaborate outside of code </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" data-analytics-event="{"location":"navbar","action":"code_search","context":"product","tag":"link","label":"code_search_link_product_navbar"}" href="https://github.com/features/code-search"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" 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 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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> </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"> Solutions <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 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Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L9.94 8 6.22 4.28a.75.75 0 0 1 0-1.06Z"></path> </svg> </a> </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"> Resources <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 flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul 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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 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":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <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":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </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="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"trending","context":"open_source","tag":"link","label":"trending_link_open_source_navbar"}" href="https://github.com/trending"> Trending </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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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="ll+5i2i4T0umY6tGhqE/mYvbuviRJS+uBQncja1+WtM/53PksqKpZFBAgoZLT71hJ9GaHtPVzHHTCNgTEIyt3w==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" 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repository" data-turbo-replace="true" title="57" data-view-component="true" class="Counter js-social-count">57</span> </a></div> </li> </ul> </div> </div> <div id="responsive-meta-container" data-turbo-replace> <div class="d-block d-md-none mb-2 px-3 px-md-4 px-lg-5"> <p class="f4 mb-3 "> Official implementation of "Align and Distill: Unifying and Improving Domain Adaptive Object Detection" (TMLR 2025) </p> <div class="mb-2 d-flex flex-items-center Link--secondary"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <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> <span class="flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a title="https://aldi-daod.github.io/" role="link" target="_blank" class="text-bold" rel="noopener noreferrer" href="https://aldi-daod.github.io/">aldi-daod.github.io/</a> </span> </div> <div class="mb-3"> <a class="Link--secondary no-underline mr-3" href="/justinkay/aldi/stargazers"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-1"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <span class="text-bold">57</span> stars </a> <a class="Link--secondary no-underline mr-3" href="/justinkay/aldi/forks"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-1"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <span class="text-bold">9</span> forks </a> <a class="Link--secondary no-underline mr-3 d-inline-block" href="/justinkay/aldi/branches"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-git-branch mr-1"> <path d="M9.5 3.25a2.25 2.25 0 1 1 3 2.122V6A2.5 2.5 0 0 1 10 8.5H6a1 1 0 0 0-1 1v1.128a2.251 2.251 0 1 1-1.5 0V5.372a2.25 2.25 0 1 1 1.5 0v1.836A2.493 2.493 0 0 1 6 7h4a1 1 0 0 0 1-1v-.628A2.25 2.25 0 0 1 9.5 3.25Zm-6 0a.75.75 0 1 0 1.5 0 .75.75 0 0 0-1.5 0Zm8.25-.75a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5ZM4.25 12a.75.75 0 1 0 0 1.5.75.75 0 0 0 0-1.5Z"></path> </svg> <span>Branches</span> </a> <a class="Link--secondary no-underline d-inline-block" href="/justinkay/aldi/tags"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag mr-1"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 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dir=\"auto\"\u003e Align and Distill: Unifying and Improving \u003cbr\u003eDomain Adaptive Object Detection\u003c/div\u003e\u003c/h1\u003e\u003ca id=\"user-content--align-and-distill-unifying-and-improving-domain-adaptive-object-detection\" class=\"anchor\" aria-label=\"Permalink: Align and Distill: Unifying and Improving Domain Adaptive Object Detection\" href=\"#-align-and-distill-unifying-and-improving-domain-adaptive-object-detection\"\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\u003cdiv align=\"center\" dir=\"auto\"\u003e\n\u003cp dir=\"auto\"\u003eOfficial codebase for \u003ca href=\"https://arxiv.org/abs/2403.12029\" rel=\"nofollow\"\u003eAlign and Distill: Unifying and Improving Domain Adaptive Object Detection\u003c/a\u003e (TMLR 2025).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e[\u003ca href=\"https://aldi-daod.github.io/\" rel=\"nofollow\"\u003eProject Page\u003c/a\u003e] [\u003ca href=\"https://arxiv.org/abs/2403.12029\" rel=\"nofollow\"\u003eArxiv\u003c/a\u003e] [\u003ca href=\"https://arxiv.org/pdf/2403.12029.pdf\" rel=\"nofollow\"\u003ePDF\u003c/a\u003e]\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://paperswithcode.com/sota/unsupervised-domain-adaptation-on-cityscapes-1?p=align-and-distill-unifying-and-improving\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b087f9b511896f95ca7024f438131a35d55d67d7a3d1a86bb5572d6b311a25ce/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e742e7376673f75726c3d68747470733a2f2f70617065727377697468636f64652e636f6d2f62616467652f616c69676e2d616e642d64697374696c6c2d756e696679696e672d616e642d696d70726f76696e672f756e737570657276697365642d646f6d61696e2d61646170746174696f6e2d6f6e2d636974797363617065732d31\" alt=\"PWC\" data-canonical-src=\"https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/align-and-distill-unifying-and-improving/unsupervised-domain-adaptation-on-cityscapes-1\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://paperswithcode.com/sota/unsupervised-domain-adaptation-on-sim10k-to-3?p=align-and-distill-unifying-and-improving\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d53f11333b4b2f0a19c985ddea99c2d1442a719f7eb72a76f3a443b33e474548/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e742e7376673f75726c3d68747470733a2f2f70617065727377697468636f64652e636f6d2f62616467652f616c69676e2d616e642d64697374696c6c2d756e696679696e672d616e642d696d70726f76696e672f756e737570657276697365642d646f6d61696e2d61646170746174696f6e2d6f6e2d73696d31306b2d746f2d33\" alt=\"PWC\" data-canonical-src=\"https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/align-and-distill-unifying-and-improving/unsupervised-domain-adaptation-on-sim10k-to-3\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://paperswithcode.com/sota/unsupervised-domain-adaptation-on-cfc-daod?p=align-and-distill-unifying-and-improving\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e644ea267cbdb8a72ca0622deea71bf0739fe21631f545eea71e55953a2e8efc/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e742e7376673f75726c3d68747470733a2f2f70617065727377697468636f64652e636f6d2f62616467652f616c69676e2d616e642d64697374696c6c2d756e696679696e672d616e642d696d70726f76696e672f756e737570657276697365642d646f6d61696e2d61646170746174696f6e2d6f6e2d6366632d64616f64\" alt=\"PWC\" data-canonical-src=\"https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/align-and-distill-unifying-and-improving/unsupervised-domain-adaptation-on-cfc-daod\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/justinkay/aldi/blob/main/docs/aldi_banner_4.png\"\u003e\u003cimg src=\"/justinkay/aldi/raw/main/docs/aldi_banner_4.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUpdates\u003c/h2\u003e\u003ca id=\"user-content-updates\" class=\"anchor\" aria-label=\"Permalink: Updates\" href=\"#updates\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e03/17/2025:\u003c/strong\u003e Accepted to \u003ca href=\"https://jmlr.org/tmlr/papers/\" rel=\"nofollow\"\u003eTransactions on Machine Learning Research\u003c/a\u003e with a Featured Certification (Spotlight)! New additions include:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eALDI++ implementations for \u003ca href=\"https://github.com/justinkay/aldi/tree/main/aldi/detr\"\u003eDETR\u003c/a\u003e and \u003ca href=\"https://github.com/justinkay/aldi/tree/main/aldi/yolo\"\u003eYOLO\u003c/a\u003e, which are SOTA for DETR and YOLO-based DAOD without additional hyperparameter tuning.\u003c/li\u003e\n\u003cli\u003eLarger \u003ca href=\"https://github.com/justinkay/aldi/blob/main/aldi/backbone.py\"\u003ebackbones\u003c/a\u003e (Vit-L, ConvNeXt).\u003c/li\u003e\n\u003c/ul\u003e\n \n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\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\"\u003eAlign and Distill (ALDI) is a state-of-the-art framework for domain adaptive object detection (DAOD).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eALDI is built on top of the \u003ca href=\"https://github.com/facebookresearch/detectron2/\"\u003eDetectron2\u003c/a\u003e object detection library and follows the same design patterns where possible. In particular, training settings are managed by \u003ca href=\"/justinkay/aldi/blob/main/configs\"\u003econfig files\u003c/a\u003e, datasets are managed by a \u003ca href=\"/justinkay/aldi/blob/main/aldi/datasets.py\"\u003edataset registry\u003c/a\u003e, training is handled by a custom \u003ca href=\"/justinkay/aldi/blob/main/aldi/trainer.py\"\u003e\u003ccode\u003eTrainer\u003c/code\u003e\u003c/a\u003e class that extends \u003ca href=\"https://github.com/facebookresearch/detectron2/blob/0ae803b1449cd2d3f8fa1b7c0f59356db10b3083/detectron2/engine/defaults.py#L323\"\u003e\u003ccode\u003eDefaultTrainer\u003c/code\u003e\u003c/a\u003e, and we provide a training script in \u003ca href=\"/justinkay/aldi/blob/main/tools/train_net.py\"\u003etools/train_net.py\u003c/a\u003e that comes with \u003ca href=\"https://detectron2.readthedocs.io/en/latest/tutorials/getting_started.html\" rel=\"nofollow\"\u003eall the same functionality\u003c/a\u003e as the \u003ca href=\"https://github.com/facebookresearch/detectron2/blob/main/tools/train_net.py\"\u003eDetectron2 script\u003c/a\u003e by the same name.\u003c/p\u003e\n\n\u003csummary\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall\u003c/h3\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\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\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e1. Install PyTorch and torchvision.\u003c/strong\u003e Follow the \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003eofficial install guide\u003c/a\u003e to install the correct versions for your CUDA version.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e2. Install ALDI.\u003c/strong\u003e Clone this repository and run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install -e .\"\u003e\u003cpre\u003epip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOptionally include the \u003ccode\u003e--no-cache-dir\u003c/code\u003e flag if you run into OOM issues.\u003c/p\u003e\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e3. (Optional) Install YOLO and DETR dependencies\u003c/b\u003e\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eWithin the ALDI directory, run\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"git submodule update --init --recursive\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003egit submodule update --init --recursive\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eto clone the relevant submodules for YOLO and DETR.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor YOLO, some additional libraries are needed:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pip install pandas requests ipython psutil seaborn\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epip install pandas requests ipython psutil seaborn\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor DETR, you must perform an additional step to build the custom \u003ccode\u003eMultiScaleDeformableAttention\u003c/code\u003e operation. Note that CUDA/GPU access is required to run this script. \u003cstrong\u003eThis has been successfully tested with PyTorch 2.5.1 but has known issues with 2.6.0.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cd aldi/detr/libs/DeformableDETRDetectron2/deformable_detr/models/ops\nbash make.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ecd aldi/detr/libs/DeformableDETRDetectron2/deformable_detr/models/ops\nbash make.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003csummary\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eData setup\u003c/h3\u003e\u003ca id=\"user-content-data-setup\" class=\"anchor\" aria-label=\"Permalink: Data setup\" href=\"#data-setup\"\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\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eThere are three kinds of \"datasets\" in domain adaptive object detection:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTrain (source)\u003c/th\u003e\n\u003cth\u003eUnlabeled (target)\u003c/th\u003e\n\u003cth\u003eTest (source or target)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLabeled source-domain images. Used for source-only baseline training, supervised burn-in, and domain-adaptive training.\u003c/td\u003e\n\u003ctd\u003eTarget-domain images that are optionally labeled. If unlabeled, used for domain-adaptive training only. If labeled, can be used to train \"oracle\" methods. Any labels are ignored during domain-adaptive training.\u003c/td\u003e\n\u003ctd\u003eA labeled source- or target-domain validation set. In most DAOD papers this comes from the target domain, even though this breaks the constraints of unsupervised domain adaptation.\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\u003cstrong\u003eSet up DAOD benchmarks (Cityscapes, Sim10k, CFC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFollow \u003ca href=\"/justinkay/aldi/blob/main/docs/DATASETS.md\"\u003ethese instructions\u003c/a\u003e to set up data and reproduce benchmark results on the datasets in \u003ca href=\"https://arxiv.org/abs/2403.12029\" rel=\"nofollow\"\u003eour paper\u003c/a\u003e: Cityscapes → Foggy Cityscapes, Sim10k → Cityscapes, and CFC Kenai → Channel.\u003c/p\u003e\n\u003cdetails\u003e\n \u003csummary\u003e\u003cb\u003eCustom datasets (use your own data)\u003c/b\u003e\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eThe easiest way to use your own dataset is to create a \u003ca href=\"https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html\" rel=\"nofollow\"\u003eCOCO-formatted JSON files\u003c/a\u003e and \u003ca href=\"https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html#register-a-coco-format-dataset\" rel=\"nofollow\"\u003eregister your datasets with Detectron2\u003c/a\u003e. You will register each separately:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# add this to the top of tools/train_net.py or aldi/datasets.py\nfrom detectron2.data.datasets import register_coco_instances\nregister_coco_instances(\u0026quot;your_train_dataset_name\u0026quot;, {}, \u0026quot;path/to/your_train_coco_labels.json\u0026quot;, \u0026quot;path/to/your/train/images/\u0026quot;)\nregister_coco_instances(\u0026quot;your_unlabeled_dataset_name\u0026quot;, {}, \u0026quot;path/to/your_unlabeled_coco_labels.json\u0026quot;, \u0026quot;path/to/your/unlabeled/images/\u0026quot;)\nregister_coco_instances(\u0026quot;your_test_dataset_name\u0026quot;, {}, \u0026quot;path/to/your_test_coco_labels.json\u0026quot;, \u0026quot;path/to/your/test/images/\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# add this to the top of tools/train_net.py or aldi/datasets.py\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edetectron2\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eregister_coco_instances\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eregister_coco_instances\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"your_train_dataset_name\"\u003c/span\u003e, {}, \u003cspan class=\"pl-s\"\u003e\"path/to/your_train_coco_labels.json\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"path/to/your/train/images/\"\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003eregister_coco_instances\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"your_unlabeled_dataset_name\"\u003c/span\u003e, {}, \u003cspan class=\"pl-s\"\u003e\"path/to/your_unlabeled_coco_labels.json\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"path/to/your/unlabeled/images/\"\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003eregister_coco_instances\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"your_test_dataset_name\"\u003c/span\u003e, {}, \u003cspan class=\"pl-s\"\u003e\"path/to/your_test_coco_labels.json\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"path/to/your/test/images/\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote that by default Detectron2 assumes all paths are relative to \u003ccode\u003e./datasets\u003c/code\u003e relative to your current working directory. You can change this location if desired using the \u003ccode\u003eDETECTRON2_DATASETS\u003c/code\u003e environment variable, e.g.: \u003ccode\u003eexport DETECTRON2_DATASETS=/path/to/datasets\u003c/code\u003e.\u003c/p\u003e\n\u003c/details\u003e\n\u003csummary\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining\u003c/h3\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\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eSee our \u003ca href=\"/justinkay/aldi/blob/main/docs/TRAINING.md\"\u003edetailed training instructions\u003c/a\u003e. The TL;DR is:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eConfig setup\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTraining is managed through \u003ca href=\"/justinkay/aldi/blob/main/configs\"\u003econfig files\u003c/a\u003e. We provide example configs for burn-in/baseline models, oracle models, and ALDI++.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou will need to modify (at least) the following values for any custom data:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"DATASETS:\n TRAIN: (\u0026quot;your_training_dataset_name\u0026quot;,) # needs to be a tuple, and can contain multiple datasets if you want\n UNLABELED: (\u0026quot;your_unlabeled_dataset_name\u0026quot;,) # needs to be a tuple, and can contain multiple datasets if you want\n TEST: (\u0026quot;your_test_dataset_name\u0026quot;,) # needs to be a tuple, and can contain multiple datasets if you want\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eDATASETS:\n TRAIN: (\"your_training_dataset_name\",) # needs to be a tuple, and can contain multiple datasets if you want\n UNLABELED: (\"your_unlabeled_dataset_name\",) # needs to be a tuple, and can contain multiple datasets if you want\n TEST: (\"your_test_dataset_name\",) # needs to be a tuple, and can contain multiple datasets if you want\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"MODEL:\n ROI_HEADS:\n NUM_CLASSES: 9 # change to match your number of classes\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eMODEL:\n ROI_HEADS:\n NUM_CLASSES: 9 # change to match your number of classes\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRun training\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eALDI involves two training phases: (1) burn-in, (2) domain adaptation. Again, please reference the \u003ca href=\"/justinkay/aldi/blob/main/docs/TRAINING.md\"\u003edetailed training instructions\u003c/a\u003e. Training involves running \u003ca href=\"/justinkay/aldi/blob/tools/train_net.py\"\u003etools/train_net.py\u003c/a\u003e for each training phase:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python tools/train_net.py --config path/to/your/config.yaml\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython tools/train_net.py --config path/to/your/config.yaml\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe script is compatible with all Detectron2 training options (\u003ccode\u003e--num-gpus\u003c/code\u003e, in-line config modifications, etc.).\u003c/p\u003e\n\u003csummary\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvaluation\u003c/h3\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\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\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003eAfter training, to run evaluation with your model:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python tools/train_net.py --eval-only --config-file path/to/your/aldi_config.yaml MODEL.WEIGHTS path/to/your/model_best.pth\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython tools/train_net.py --eval-only --config-file path/to/your/aldi_config.yaml MODEL.WEIGHTS path/to/your/model_best.pth\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\"\u003eModel zoo\u003c/h2\u003e\u003ca id=\"user-content-model-zoo\" class=\"anchor\" aria-label=\"Permalink: Model zoo\" href=\"#model-zoo\"\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\"\u003eWe provide burn-in checkpoints and final models for DAOD benchmarks (Cityscapes → Foggy Cityscapes, Sim10k → Cityscapes, and CFC Kenai → Channel) in \u003ca href=\"/justinkay/aldi/blob/main/docs/MODELS.md\"\u003ethe model zoo\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can download the required model weights for any config file we provide using\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python tools/download_model_for_config.py --config-file path/to/config.yaml\"\u003e\u003cpre\u003epython tools/download_model_for_config.py --config-file path/to/config.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExtras and prior work\u003c/h2\u003e\u003ca id=\"user-content-extras-and-prior-work\" class=\"anchor\" aria-label=\"Permalink: Extras and prior work\" href=\"#extras-and-prior-work\"\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 \u003ca href=\"/justinkay/aldi/blob/main/justinkay/aldi/tree/main\"\u003emain\u003c/a\u003e branch contains all you need to run ALDI, and is a good starting point for most.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAdditional code and configuration files to reproduce all experiments in \u003ca href=\"https://arxiv.org/abs/2403.12029\" rel=\"nofollow\"\u003eour paper\u003c/a\u003e can be found on the \u003ca href=\"/justinkay/aldi/blob/main/justinkay/aldi/tree/extras\"\u003eextras\u003c/a\u003e branch.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReference\u003c/h2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-label=\"Permalink: Reference\" href=\"#reference\"\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\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ca href=\"https://arxiv.org/abs/2403.12029\" rel=\"nofollow\"\u003eAlign and Distill: Unifying and Improving Domain Adaptive Object Detection\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-align-and-distill-unifying-and-improving-domain-adaptive-object-detection\" class=\"anchor\" aria-label=\"Permalink: Align and Distill: Unifying and Improving Domain Adaptive Object Detection\" href=\"#align-and-distill-unifying-and-improving-domain-adaptive-object-detection\"\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\u003ca href=\"https://justinkay.github.io\" rel=\"nofollow\"\u003eJustin Kay\u003c/a\u003e, \u003ca href=\"https://timm.haucke.xyz/\" rel=\"nofollow\"\u003eTimm Haucke\u003c/a\u003e, \u003ca href=\"https://suzanne-stathatos.github.io/\" rel=\"nofollow\"\u003eSuzanne Stathatos\u003c/a\u003e, \u003ca href=\"https://www.amazon.science/author/siqi-deng\" rel=\"nofollow\"\u003eSiqi Deng\u003c/a\u003e, \u003ca href=\"https://home.tu.org/users/erikyoung\" rel=\"nofollow\"\u003eErik Young\u003c/a\u003e, \u003ca href=\"https://scholar.google.com/citations?user=j29kMCwAAAAJ\" rel=\"nofollow\"\u003ePietro Perona\u003c/a\u003e, \u003ca href=\"https://beerys.github.io/\" rel=\"nofollow\"\u003eSara Beery\u003c/a\u003e, and \u003ca href=\"https://gvanhorn38.github.io/\" rel=\"nofollow\"\u003eGrant Van Horn\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eObject detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on addressing this challenge. Unfortunately, we identify systemic benchmarking pitfalls that call past results into question and hamper further progress: (a) Overestimation of performance due to underpowered baselines, (b) Inconsistent implementation practices preventing transparent comparisons of methods, and (c) Lack of generality due to outdated backbones and lack of diversity in benchmarks. We address these problems by introducing: (1) A unified benchmarking and implementation framework, Align and Distill (ALDI), enabling comparison of DAOD methods and supporting future development, (2) A fair and modern training and evaluation protocol for DAOD that addresses benchmarking pitfalls, (3) A new DAOD benchmark dataset, CFC-DAOD, enabling evaluation on diverse real-world data, and (4) A new method, ALDI++, that achieves state-of-the-art results by a large margin. ALDI++ outperforms the previous state-of-the-art by +3.5 AP50 on Cityscapes to Foggy Cityscapes, +5.7 AP50 on Sim10k to Cityscapes (where ours is the only method to outperform a fair baseline), and +0.6 AP50 on CFC Kenai to Channel. ALDI and ALDI++ are architecture-agnostic, setting a new state-of-the-art for YOLO and DETR-based DAOD as well without additional hyperparameter tuning. Our framework, dataset, and state-of-the-art method offer a critical reset for DAOD and provide a strong foundation for future research.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you find our work useful in your research please consider citing our paper:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{\nkay2025align,\ntitle={Align and Distill: Unifying and Improving Domain Adaptive Object Detection},\nauthor={Justin Kay and Timm Haucke and Suzanne Stathatos and Siqi Deng and Erik Young and Pietro Perona and Sara Beery and Grant Van Horn},\njournal={Transactions on Machine Learning Research},\nissn={2835-8856},\nyear={2025},\nurl={https://openreview.net/forum?id=ssXSrZ94sR},\nnote={Featured Certification}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{\nkay2025align,\ntitle={Align and Distill: Unifying and Improving Domain Adaptive Object Detection},\nauthor={Justin Kay and Timm Haucke and Suzanne Stathatos and Siqi Deng and Erik Young and Pietro Perona and Sara Beery and Grant Van Horn},\njournal={Transactions on Machine Learning Research},\nissn={2835-8856},\nyear={2025},\nurl={https://openreview.net/forum?id=ssXSrZ94sR},\nnote={Featured Certification}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":" Align and Distill: Unifying and Improving Domain Adaptive Object Detection","anchor":"-align-and-distill-unifying-and-improving-domain-adaptive-object-detection","htmlText":" Align and Distill: Unifying and Improving Domain Adaptive Object Detection"},{"level":2,"text":"Updates","anchor":"updates","htmlText":"Updates"},{"level":2,"text":"Documentation","anchor":"documentation","htmlText":"Documentation"},{"level":3,"text":"Install","anchor":"install","htmlText":"Install"},{"level":3,"text":"Data setup","anchor":"data-setup","htmlText":"Data setup"},{"level":3,"text":"Training","anchor":"training","htmlText":"Training"},{"level":3,"text":"Evaluation","anchor":"evaluation","htmlText":"Evaluation"},{"level":2,"text":"Model zoo","anchor":"model-zoo","htmlText":"Model zoo"},{"level":2,"text":"Extras and prior work","anchor":"extras-and-prior-work","htmlText":"Extras and prior work"},{"level":2,"text":"Reference","anchor":"reference","htmlText":"Reference"},{"level":4,"text":"Align and Distill: Unifying and Improving Domain Adaptive Object Detection","anchor":"align-and-distill-unifying-and-improving-domain-adaptive-object-detection","htmlText":"Align and Distill: Unifying and Improving Domain Adaptive Object 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data-hotkey="k"></button><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading" id="folders-and-files">Folders and files</h2><table aria-labelledby="folders-and-files" class="Box-sc-g0xbh4-0 fdROMU"><thead class="Box-sc-g0xbh4-0 jGKpsv"><tr class="Box-sc-g0xbh4-0 jdgHnn"><th colSpan="2" class="Box-sc-g0xbh4-0 bQivRW"><span class="text-bold">Name</span></th><th colSpan="1" class="Box-sc-g0xbh4-0 ldkMIO"><span class="text-bold">Name</span></th><th class="hide-sm"><div title="Last commit message" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit message</span></div></th><th colSpan="1" class="Box-sc-g0xbh4-0 jMbWeI"><div title="Last commit date" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit date</span></div></th></tr></thead><tbody><tr class="Box-sc-g0xbh4-0 gpqjiB"><td colSpan="3" class="bgColor-muted p-1 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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" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><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">259 Commits</span></span></span></a><div class="d-sm-none"></div><div class="d-flex d-lg-none"><span role="tooltip" aria-label="259 Commits" id="history-icon-button-tooltip" class="Tooltip__TooltipBase-sc-17tf59c-0 hWlpPn tooltipped-n"><a 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class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="aldi" aria-label="aldi, (Directory)" class="Link--primary" href="/justinkay/aldi/tree/main/aldi">aldi</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-1"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="configs" aria-label="configs, (Directory)" class="Link--primary" href="/justinkay/aldi/tree/main/configs">configs</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="configs" aria-label="configs, (Directory)" class="Link--primary" href="/justinkay/aldi/tree/main/configs">configs</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-2"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg 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display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="tests" aria-label="tests, (Directory)" class="Link--primary" href="/justinkay/aldi/tree/main/tests">tests</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 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aria-describedby=":Rr9ab:-loading-announcement" id=":Rr9ab:"><svg aria-hidden="true" focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 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"><h1 tabindex="-1" class="heading-element" dir="auto"><div align="center" dir="auto"> Align and Distill: Unifying and Improving <br>Domain Adaptive Object Detection</div></h1><a id="user-content--align-and-distill-unifying-and-improving-domain-adaptive-object-detection" class="anchor" aria-label="Permalink: Align and Distill: Unifying and Improving Domain Adaptive Object Detection" href="#-align-and-distill-unifying-and-improving-domain-adaptive-object-detection"><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> <div align="center" dir="auto"> <p dir="auto">Official codebase for <a href="https://arxiv.org/abs/2403.12029" rel="nofollow">Align and Distill: Unifying and Improving Domain Adaptive Object Detection</a> (TMLR 2025).</p> <p dir="auto">[<a href="https://aldi-daod.github.io/" rel="nofollow">Project Page</a>] [<a href="https://arxiv.org/abs/2403.12029" rel="nofollow">Arxiv</a>] [<a href="https://arxiv.org/pdf/2403.12029.pdf" rel="nofollow">PDF</a>]</p> <p dir="auto"><a href="https://paperswithcode.com/sota/unsupervised-domain-adaptation-on-cityscapes-1?p=align-and-distill-unifying-and-improving" rel="nofollow"><img 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class="anchor" aria-label="Permalink: Updates" href="#updates"><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> <ul dir="auto"> <li> <p dir="auto"><strong>03/17/2025:</strong> Accepted to <a href="https://jmlr.org/tmlr/papers/" rel="nofollow">Transactions on Machine Learning Research</a> with a Featured Certification (Spotlight)! New additions include:</p> <ul dir="auto"> <li>ALDI++ implementations for <a href="https://github.com/justinkay/aldi/tree/main/aldi/detr">DETR</a> and <a href="https://github.com/justinkay/aldi/tree/main/aldi/yolo">YOLO</a>, which are SOTA for DETR and YOLO-based DAOD without additional hyperparameter tuning.</li> <li>Larger <a href="https://github.com/justinkay/aldi/blob/main/aldi/backbone.py">backbones</a> (Vit-L, ConvNeXt).</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Documentation</h2><a id="user-content-documentation" class="anchor" aria-label="Permalink: Documentation" href="#documentation"><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">Align and Distill (ALDI) is a state-of-the-art framework for domain adaptive object detection (DAOD).</p> <p dir="auto">ALDI is built on top of the <a href="https://github.com/facebookresearch/detectron2/">Detectron2</a> object detection library and follows the same design patterns where possible. In particular, training settings are managed by <a href="/justinkay/aldi/blob/main/configs">config files</a>, datasets are managed by a <a href="/justinkay/aldi/blob/main/aldi/datasets.py">dataset registry</a>, training is handled by a custom <a href="/justinkay/aldi/blob/main/aldi/trainer.py"><code>Trainer</code></a> class that extends <a href="https://github.com/facebookresearch/detectron2/blob/0ae803b1449cd2d3f8fa1b7c0f59356db10b3083/detectron2/engine/defaults.py#L323"><code>DefaultTrainer</code></a>, and we provide a training script in <a href="/justinkay/aldi/blob/main/tools/train_net.py">tools/train_net.py</a> that comes with <a href="https://detectron2.readthedocs.io/en/latest/tutorials/getting_started.html" rel="nofollow">all the same functionality</a> as the <a href="https://github.com/facebookresearch/detectron2/blob/main/tools/train_net.py">Detectron2 script</a> by the same name.</p> <summary><div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Install</h3><a id="user-content-install" class="anchor" aria-label="Permalink: Install" href="#install"><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></summary> <p dir="auto"><strong>1. Install PyTorch and torchvision.</strong> Follow the <a href="https://pytorch.org/get-started/locally/" rel="nofollow">official install guide</a> to install the correct versions for your CUDA version.</p> <p dir="auto"><strong>2. Install ALDI.</strong> Clone this repository and run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install -e ."><pre>pip install -e <span class="pl-c1">.</span></pre></div> <p dir="auto">Optionally include the <code>--no-cache-dir</code> flag if you run into OOM issues.</p> <details> <summary><b>3. (Optional) Install YOLO and DETR dependencies</b></summary> <p dir="auto">Within the ALDI directory, run</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="git submodule update --init --recursive"><pre class="notranslate"><code>git submodule update --init --recursive </code></pre></div> <p dir="auto">to clone the relevant submodules for YOLO and DETR.</p> <p dir="auto">For YOLO, some additional libraries are needed:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pip install pandas requests ipython psutil seaborn"><pre class="notranslate"><code>pip install pandas requests ipython psutil seaborn </code></pre></div> <p dir="auto">For DETR, you must perform an additional step to build the custom <code>MultiScaleDeformableAttention</code> operation. Note that CUDA/GPU access is required to run this script. <strong>This has been successfully tested with PyTorch 2.5.1 but has known issues with 2.6.0.</strong></p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cd aldi/detr/libs/DeformableDETRDetectron2/deformable_detr/models/ops bash make.sh"><pre class="notranslate"><code>cd aldi/detr/libs/DeformableDETRDetectron2/deformable_detr/models/ops bash make.sh </code></pre></div> </details> <summary><div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Data setup</h3><a id="user-content-data-setup" class="anchor" aria-label="Permalink: Data setup" href="#data-setup"><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></summary> <p dir="auto">There are three kinds of "datasets" in domain adaptive object detection:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Train (source)</th> <th>Unlabeled (target)</th> <th>Test (source or target)</th> </tr> </thead> <tbody> <tr> <td>Labeled source-domain images. Used for source-only baseline training, supervised burn-in, and domain-adaptive training.</td> <td>Target-domain images that are optionally labeled. If unlabeled, used for domain-adaptive training only. If labeled, can be used to train "oracle" methods. Any labels are ignored during domain-adaptive training.</td> <td>A labeled source- or target-domain validation set. In most DAOD papers this comes from the target domain, even though this breaks the constraints of unsupervised domain adaptation.</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto"><strong>Set up DAOD benchmarks (Cityscapes, Sim10k, CFC)</strong></p> <p dir="auto">Follow <a href="/justinkay/aldi/blob/main/docs/DATASETS.md">these instructions</a> to set up data and reproduce benchmark results on the datasets in <a href="https://arxiv.org/abs/2403.12029" rel="nofollow">our paper</a>: Cityscapes → Foggy Cityscapes, Sim10k → Cityscapes, and CFC Kenai → Channel.</p> <details> <summary><b>Custom datasets (use your own data)</b></summary> <p dir="auto">The easiest way to use your own dataset is to create a <a href="https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html" rel="nofollow">COCO-formatted JSON files</a> and <a href="https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html#register-a-coco-format-dataset" rel="nofollow">register your datasets with Detectron2</a>. You will register each separately:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# add this to the top of tools/train_net.py or aldi/datasets.py from detectron2.data.datasets import register_coco_instances register_coco_instances("your_train_dataset_name", {}, "path/to/your_train_coco_labels.json", "path/to/your/train/images/") register_coco_instances("your_unlabeled_dataset_name", {}, "path/to/your_unlabeled_coco_labels.json", "path/to/your/unlabeled/images/") register_coco_instances("your_test_dataset_name", {}, "path/to/your_test_coco_labels.json", "path/to/your/test/images/")"><pre><span class="pl-c"># add this to the top of tools/train_net.py or aldi/datasets.py</span> <span class="pl-k">from</span> <span class="pl-s1">detectron2</span>.<span class="pl-s1">data</span>.<span class="pl-s1">datasets</span> <span class="pl-k">import</span> <span class="pl-s1">register_coco_instances</span> <span class="pl-en">register_coco_instances</span>(<span class="pl-s">"your_train_dataset_name"</span>, {}, <span class="pl-s">"path/to/your_train_coco_labels.json"</span>, <span class="pl-s">"path/to/your/train/images/"</span>) <span class="pl-en">register_coco_instances</span>(<span class="pl-s">"your_unlabeled_dataset_name"</span>, {}, <span class="pl-s">"path/to/your_unlabeled_coco_labels.json"</span>, <span class="pl-s">"path/to/your/unlabeled/images/"</span>) <span class="pl-en">register_coco_instances</span>(<span class="pl-s">"your_test_dataset_name"</span>, {}, <span class="pl-s">"path/to/your_test_coco_labels.json"</span>, <span class="pl-s">"path/to/your/test/images/"</span>)</pre></div> <p dir="auto">Note that by default Detectron2 assumes all paths are relative to <code>./datasets</code> relative to your current working directory. You can change this location if desired using the <code>DETECTRON2_DATASETS</code> environment variable, e.g.: <code>export DETECTRON2_DATASETS=/path/to/datasets</code>.</p> </details> <summary><div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Training</h3><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></summary> <p dir="auto">See our <a href="/justinkay/aldi/blob/main/docs/TRAINING.md">detailed training instructions</a>. The TL;DR is:</p> <p dir="auto"><strong>Config setup</strong></p> <p dir="auto">Training is managed through <a href="/justinkay/aldi/blob/main/configs">config files</a>. We provide example configs for burn-in/baseline models, oracle models, and ALDI++.</p> <p dir="auto">You will need to modify (at least) the following values for any custom data:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="DATASETS: TRAIN: ("your_training_dataset_name",) # needs to be a tuple, and can contain multiple datasets if you want UNLABELED: ("your_unlabeled_dataset_name",) # needs to be a tuple, and can contain multiple datasets if you want TEST: ("your_test_dataset_name",) # needs to be a tuple, and can contain multiple datasets if you want"><pre class="notranslate"><code>DATASETS: TRAIN: ("your_training_dataset_name",) # needs to be a tuple, and can contain multiple datasets if you want UNLABELED: ("your_unlabeled_dataset_name",) # needs to be a tuple, and can contain multiple datasets if you want TEST: ("your_test_dataset_name",) # needs to be a tuple, and can contain multiple datasets if you want </code></pre></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="MODEL: ROI_HEADS: NUM_CLASSES: 9 # change to match your number of classes"><pre class="notranslate"><code>MODEL: ROI_HEADS: NUM_CLASSES: 9 # change to match your number of classes </code></pre></div> <p dir="auto"><strong>Run training</strong></p> <p dir="auto">ALDI involves two training phases: (1) burn-in, (2) domain adaptation. Again, please reference the <a href="/justinkay/aldi/blob/main/docs/TRAINING.md">detailed training instructions</a>. Training involves running <a href="/justinkay/aldi/blob/tools/train_net.py">tools/train_net.py</a> for each training phase:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python tools/train_net.py --config path/to/your/config.yaml"><pre class="notranslate"><code>python tools/train_net.py --config path/to/your/config.yaml </code></pre></div> <p dir="auto">The script is compatible with all Detectron2 training options (<code>--num-gpus</code>, in-line config modifications, etc.).</p> <summary><div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Evaluation</h3><a id="user-content-evaluation" class="anchor" aria-label="Permalink: Evaluation" href="#evaluation"><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></summary> <p dir="auto">After training, to run evaluation with your model:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python tools/train_net.py --eval-only --config-file path/to/your/aldi_config.yaml MODEL.WEIGHTS path/to/your/model_best.pth"><pre class="notranslate"><code>python tools/train_net.py --eval-only --config-file path/to/your/aldi_config.yaml MODEL.WEIGHTS path/to/your/model_best.pth </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Model zoo</h2><a id="user-content-model-zoo" class="anchor" aria-label="Permalink: Model zoo" href="#model-zoo"><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">We provide burn-in checkpoints and final models for DAOD benchmarks (Cityscapes → Foggy Cityscapes, Sim10k → Cityscapes, and CFC Kenai → Channel) in <a href="/justinkay/aldi/blob/main/docs/MODELS.md">the model zoo</a>.</p> <p dir="auto">You can download the required model weights for any config file we provide using</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python tools/download_model_for_config.py --config-file path/to/config.yaml"><pre>python tools/download_model_for_config.py --config-file path/to/config.yaml</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Extras and prior work</h2><a id="user-content-extras-and-prior-work" class="anchor" aria-label="Permalink: Extras and prior work" href="#extras-and-prior-work"><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 <a href="/justinkay/aldi/blob/main/justinkay/aldi/tree/main">main</a> branch contains all you need to run ALDI, and is a good starting point for most.</p> <p dir="auto">Additional code and configuration files to reproduce all experiments in <a href="https://arxiv.org/abs/2403.12029" rel="nofollow">our paper</a> can be found on the <a href="/justinkay/aldi/blob/main/justinkay/aldi/tree/extras">extras</a> branch.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Reference</h2><a id="user-content-reference" class="anchor" aria-label="Permalink: Reference" href="#reference"><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> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto"><a href="https://arxiv.org/abs/2403.12029" rel="nofollow">Align and Distill: Unifying and Improving Domain Adaptive Object Detection</a></h4><a id="user-content-align-and-distill-unifying-and-improving-domain-adaptive-object-detection" class="anchor" aria-label="Permalink: Align and Distill: Unifying and Improving Domain Adaptive Object Detection" href="#align-and-distill-unifying-and-improving-domain-adaptive-object-detection"><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"><a href="https://justinkay.github.io" rel="nofollow">Justin Kay</a>, <a href="https://timm.haucke.xyz/" rel="nofollow">Timm Haucke</a>, <a href="https://suzanne-stathatos.github.io/" rel="nofollow">Suzanne Stathatos</a>, <a href="https://www.amazon.science/author/siqi-deng" rel="nofollow">Siqi Deng</a>, <a href="https://home.tu.org/users/erikyoung" rel="nofollow">Erik Young</a>, <a href="https://scholar.google.com/citations?user=j29kMCwAAAAJ" rel="nofollow">Pietro Perona</a>, <a href="https://beerys.github.io/" rel="nofollow">Sara Beery</a>, and <a href="https://gvanhorn38.github.io/" rel="nofollow">Grant Van Horn</a>.</p> <p dir="auto">Object detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on addressing this challenge. Unfortunately, we identify systemic benchmarking pitfalls that call past results into question and hamper further progress: (a) Overestimation of performance due to underpowered baselines, (b) Inconsistent implementation practices preventing transparent comparisons of methods, and (c) Lack of generality due to outdated backbones and lack of diversity in benchmarks. We address these problems by introducing: (1) A unified benchmarking and implementation framework, Align and Distill (ALDI), enabling comparison of DAOD methods and supporting future development, (2) A fair and modern training and evaluation protocol for DAOD that addresses benchmarking pitfalls, (3) A new DAOD benchmark dataset, CFC-DAOD, enabling evaluation on diverse real-world data, and (4) A new method, ALDI++, that achieves state-of-the-art results by a large margin. ALDI++ outperforms the previous state-of-the-art by +3.5 AP50 on Cityscapes to Foggy Cityscapes, +5.7 AP50 on Sim10k to Cityscapes (where ours is the only method to outperform a fair baseline), and +0.6 AP50 on CFC Kenai to Channel. ALDI and ALDI++ are architecture-agnostic, setting a new state-of-the-art for YOLO and DETR-based DAOD as well without additional hyperparameter tuning. Our framework, dataset, and state-of-the-art method offer a critical reset for DAOD and provide a strong foundation for future research.</p> <p dir="auto">If you find our work useful in your research please consider citing our paper:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{ kay2025align, title={Align and Distill: Unifying and Improving Domain Adaptive Object Detection}, author={Justin Kay and Timm Haucke and Suzanne Stathatos and Siqi Deng and Erik Young and Pietro Perona and Sara Beery and Grant Van Horn}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2025}, url={https://openreview.net/forum?id=ssXSrZ94sR}, note={Featured Certification} }"><pre class="notranslate"><code>@article{ kay2025align, title={Align and Distill: Unifying and Improving Domain Adaptive Object Detection}, author={Justin Kay and Timm Haucke and Suzanne Stathatos and Siqi Deng and Erik Young and Pietro Perona and Sara Beery and Grant Van Horn}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2025}, url={https://openreview.net/forum?id=ssXSrZ94sR}, note={Featured Certification} } </code></pre></div> </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="1qpbD+9Ks4higbxrC7j/ObGIAuUYs6ICWQXrAxOACuhq5/PadkhzN47p47aUWCfIGTe/OBu7vcDljGv1oQAskA==" /> </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"> Official implementation of "Align and Distill: Unifying and Improving Domain Adaptive Object Detection" (TMLR 2025) </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" 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1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>57</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/justinkay/aldi/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 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color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>