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GitHub - keras-team/keras-cv: Industry-strength Computer Vision workflows with Keras
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1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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dir=\"auto\"\u003eKerasCV\u003c/h1\u003e\u003ca id=\"user-content-kerascv\" class=\"anchor\" aria-label=\"Permalink: KerasCV\" href=\"#kerascv\"\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 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href=\"https://camo.githubusercontent.com/049bd61fb4517a65d7a479bd4723fb8f3fa2913cc6f90893d5c9691f14202201/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d76332e372e302b2d737563636573732e737667\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/049bd61fb4517a65d7a479bd4723fb8f3fa2913cc6f90893d5c9691f14202201/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d76332e372e302b2d737563636573732e737667\" alt=\"Python\" data-canonical-src=\"https://img.shields.io/badge/python-v3.7.0+-success.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-important\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-report mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z\"\u003e\u003c/path\u003e\u003c/svg\u003eImportant\u003c/p\u003e\u003cp dir=\"auto\"\u003e📢 KerasCV is moving to \u003ca href=\"https://github.com/keras-team/keras-hub/\"\u003eKerasHub\u003c/a\u003e! 📢\nRead \u003ca href=\"https://github.com/keras-team/keras-hub/issues/1831\" data-hovercard-type=\"issue\" data-hovercard-url=\"/keras-team/keras-hub/issues/1831/hovercard\"\u003ethe announcement\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eKerasCV will not continue to work as is; nothing will break. All development\nof new vision models should move to KerasHub.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eKerasCV is a library of modular computer vision components that work natively\nwith TensorFlow, JAX, or PyTorch. Built on Keras 3, these models, layers,\nmetrics, callbacks, etc., can be trained and serialized in any framework and\nre-used in another without costly migrations. See \"Configuring your backend\"\nbelow for more details on multi-framework KerasCV.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b7b12717e6c1e89048f44901d03e5bf54ba43b7f0763a007a66fec258d45dfc3/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f6b657261732d63762f6775696465732f6b657261732d63762d6175676d656e746174696f6e732e676966\"\u003e\u003cimg style=\"width: 440px; max-width: 100%;\" src=\"https://camo.githubusercontent.com/b7b12717e6c1e89048f44901d03e5bf54ba43b7f0763a007a66fec258d45dfc3/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f6b657261732d63762f6775696465732f6b657261732d63762d6175676d656e746174696f6e732e676966\" data-animated-image=\"\" data-canonical-src=\"https://storage.googleapis.com/keras-cv/guides/keras-cv-augmentations.gif\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eKerasCV can be understood as a horizontal extension of the Keras API: the\ncomponents are new first-party Keras objects that are too specialized to be\nadded to core Keras. They receive the same level of polish and backwards\ncompatibility guarantees as the core Keras API, and they are maintained by the\nKeras team.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOur APIs assist in common computer vision tasks such as data augmentation,\nclassification, object detection, segmentation, image generation, and more.\nApplied computer vision engineers can leverage KerasCV to quickly assemble\nproduction-grade, state-of-the-art training and inference pipelines for all of\nthese common tasks.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick Links\u003c/h2\u003e\u003ca id=\"user-content-quick-links\" class=\"anchor\" aria-label=\"Permalink: Quick Links\" href=\"#quick-links\"\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\u003ca href=\"https://keras.io/api/keras_cv/models/\" rel=\"nofollow\"\u003eList of available models and presets\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://keras.io/guides/keras_cv/\" rel=\"nofollow\"\u003eDeveloper Guides\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/keras-team/keras-cv/blob/master/CONTRIBUTING.md\"\u003eContributing Guide\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/keras-team/keras-cv/issues?q=is%3Aopen+is%3Aissue+label%3Acontribution-welcome\"\u003eCall for Contributions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/keras-team/keras-cv/blob/master/API_DESIGN.md\"\u003eAPI Design Guidelines\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\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\"\u003eKerasCV supports both Keras 2 and Keras 3. We recommend Keras 3 for all new\nusers, as it enables using KerasCV models and layers with JAX, TensorFlow and\nPyTorch.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKeras 2 Installation\u003c/h3\u003e\u003ca id=\"user-content-keras-2-installation\" class=\"anchor\" aria-label=\"Permalink: Keras 2 Installation\" href=\"#keras-2-installation\"\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\"\u003eTo install the latest KerasCV release with Keras 2, simply run:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pip install --upgrade keras-cv tensorflow\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epip install --upgrade keras-cv tensorflow\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKeras 3 Installation\u003c/h3\u003e\u003ca id=\"user-content-keras-3-installation\" class=\"anchor\" aria-label=\"Permalink: Keras 3 Installation\" href=\"#keras-3-installation\"\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\"\u003eThere are currently two ways to install Keras 3 with KerasCV. To install the\nlatest changes for KerasCV and Keras, you can use our nightly package.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pip install --upgrade keras-cv-nightly tf-nightly\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epip install --upgrade keras-cv-nightly tf-nightly\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install the stable versions of KerasCV and Keras 3, you should install Keras\n3 \u003cstrong\u003eafter\u003c/strong\u003e installing KerasCV. This is a temporary step while TensorFlow is\npinned to Keras 2, and will no longer be necessary after TensorFlow 2.16.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pip install --upgrade keras-cv tensorflow\npip install --upgrade keras\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epip install --upgrade keras-cv tensorflow\npip install --upgrade keras\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-important\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-report mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z\"\u003e\u003c/path\u003e\u003c/svg\u003eImportant\u003c/p\u003e\u003cp dir=\"auto\"\u003eKeras 3 will not function with TensorFlow 2.14 or earlier.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConfiguring your backend\u003c/h2\u003e\u003ca id=\"user-content-configuring-your-backend\" class=\"anchor\" aria-label=\"Permalink: Configuring your backend\" href=\"#configuring-your-backend\"\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\"\u003eIf you have Keras 3 installed in your environment (see installation above),\nyou can use KerasCV with any of JAX, TensorFlow and PyTorch. To do so, set the\n\u003ccode\u003eKERAS_BACKEND\u003c/code\u003e environment variable. For example:\nso by setting the \u003ccode\u003eKERAS_BACKEND\u003c/code\u003e environment variable. For example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export KERAS_BACKEND=jax\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e KERAS_BACKEND=jax\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOr in Colab, with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import os\nos.environ[\u0026quot;KERAS_BACKEND\u0026quot;] = \u0026quot;jax\u0026quot;\n\nimport keras_cv\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eenviron\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e\"KERAS_BACKEND\"\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"jax\"\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-important\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-report mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z\"\u003e\u003c/path\u003e\u003c/svg\u003eImportant\u003c/p\u003e\u003cp dir=\"auto\"\u003eMake sure to set the \u003ccode\u003eKERAS_BACKEND\u003c/code\u003e before import any Keras libraries, it\nwill be used to set up Keras when it is first imported.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOnce that configuration step is done, you can just import KerasCV and start\nusing it on top of your backend of choice:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import keras_cv\nimport keras\n\nfilepath = keras.utils.get_file(origin=\u0026quot;https://i.imgur.com/gCNcJJI.jpg\u0026quot;)\nimage = np.array(keras.utils.load_img(filepath))\nimage_resized = keras.ops.image.resize(image, (640, 640))[None, ...]\n\nmodel = keras_cv.models.YOLOV8Detector.from_preset(\n \u0026quot;yolo_v8_m_pascalvoc\u0026quot;,\n bounding_box_format=\u0026quot;xywh\u0026quot;,\n)\npredictions = model.predict(image_resized)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003efilepath\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eget_file\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eorigin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"https://i.imgur.com/gCNcJJI.jpg\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003earray\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eutils\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_img\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efilepath\u003c/span\u003e))\n\u003cspan class=\"pl-s1\"\u003eimage_resized\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eops\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eresize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e, (\u003cspan class=\"pl-c1\"\u003e640\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e640\u003c/span\u003e))[\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e, ...]\n\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eYOLOV8Detector\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efrom_preset\u003c/span\u003e(\n \u003cspan class=\"pl-s\"\u003e\"yolo_v8_m_pascalvoc\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ebounding_box_format\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"xywh\"\u003c/span\u003e,\n)\n\u003cspan class=\"pl-s1\"\u003epredictions\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003epredict\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimage_resized\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuickstart\u003c/h2\u003e\u003ca id=\"user-content-quickstart\" class=\"anchor\" aria-label=\"Permalink: Quickstart\" href=\"#quickstart\"\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=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import tensorflow as tf\nimport keras_cv\nimport tensorflow_datasets as tfds\nimport keras\n\n# Create a preprocessing pipeline with augmentations\nBATCH_SIZE = 16\nNUM_CLASSES = 3\naugmenter = keras_cv.layers.Augmenter(\n [\n keras_cv.layers.RandomFlip(),\n keras_cv.layers.RandAugment(value_range=(0, 255)),\n keras_cv.layers.CutMix(),\n ],\n)\n\ndef preprocess_data(images, labels, augment=False):\n labels = tf.one_hot(labels, NUM_CLASSES)\n inputs = {\u0026quot;images\u0026quot;: images, \u0026quot;labels\u0026quot;: labels}\n outputs = inputs\n if augment:\n outputs = augmenter(outputs)\n return outputs['images'], outputs['labels']\n\ntrain_dataset, test_dataset = tfds.load(\n 'rock_paper_scissors',\n as_supervised=True,\n split=['train', 'test'],\n)\ntrain_dataset = train_dataset.batch(BATCH_SIZE).map(\n lambda x, y: preprocess_data(x, y, augment=True),\n num_parallel_calls=tf.data.AUTOTUNE).prefetch(\n tf.data.AUTOTUNE)\ntest_dataset = test_dataset.batch(BATCH_SIZE).map(\n preprocess_data, num_parallel_calls=tf.data.AUTOTUNE).prefetch(\n tf.data.AUTOTUNE)\n\n# Create a model using a pretrained backbone\nbackbone = keras_cv.models.EfficientNetV2Backbone.from_preset(\n \u0026quot;efficientnetv2_b0_imagenet\u0026quot;\n)\nmodel = keras_cv.models.ImageClassifier(\n backbone=backbone,\n num_classes=NUM_CLASSES,\n activation=\u0026quot;softmax\u0026quot;,\n)\nmodel.compile(\n loss='categorical_crossentropy',\n optimizer=keras.optimizers.Adam(learning_rate=1e-5),\n metrics=['accuracy']\n)\n\n# Train your model\nmodel.fit(\n train_dataset,\n validation_data=test_dataset,\n epochs=8,\n)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etensorflow\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etensorflow_datasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etfds\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Create a preprocessing pipeline with augmentations\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003eBATCH_SIZE\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003eNUM_CLASSES\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eaugmenter\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elayers\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eAugmenter\u003c/span\u003e(\n [\n \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elayers\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eRandomFlip\u003c/span\u003e(),\n \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elayers\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eRandAugment\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003evalue_range\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e255\u003c/span\u003e)),\n \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elayers\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eCutMix\u003c/span\u003e(),\n ],\n)\n\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003epreprocess_data\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimages\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eaugment\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eone_hot\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eNUM_CLASSES\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e {\u003cspan class=\"pl-s\"\u003e\"images\"\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eimages\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"labels\"\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003elabels\u003c/span\u003e}\n \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eaugment\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eaugmenter\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e)\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'images'\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'labels'\u003c/span\u003e]\n\n\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etest_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etfds\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload\u003c/span\u003e(\n \u003cspan class=\"pl-s\"\u003e'rock_paper_scissors'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eas_supervised\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esplit\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'train'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'test'\u003c/span\u003e],\n)\n\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebatch\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eBATCH_SIZE\u003c/span\u003e).\u003cspan class=\"pl-en\"\u003emap\u003c/span\u003e(\n \u003cspan class=\"pl-k\"\u003elambda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e: \u003cspan class=\"pl-en\"\u003epreprocess_data\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eaugment\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003enum_parallel_calls\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eAUTOTUNE\u003c/span\u003e).\u003cspan class=\"pl-en\"\u003eprefetch\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eAUTOTUNE\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003etest_dataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etest_dataset\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebatch\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eBATCH_SIZE\u003c/span\u003e).\u003cspan class=\"pl-en\"\u003emap\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003epreprocess_data\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enum_parallel_calls\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eAUTOTUNE\u003c/span\u003e).\u003cspan class=\"pl-en\"\u003eprefetch\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eAUTOTUNE\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Create a model using a pretrained backbone\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ebackbone\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eEfficientNetV2Backbone\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efrom_preset\u003c/span\u003e(\n \u003cspan class=\"pl-s\"\u003e\"efficientnetv2_b0_imagenet\"\u003c/span\u003e\n)\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras_cv\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eImageClassifier\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003ebackbone\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ebackbone\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003enum_classes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eNUM_CLASSES\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eactivation\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"softmax\"\u003c/span\u003e,\n)\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ecompile\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'categorical_crossentropy'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eoptimizers\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eAdam\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elearning_rate\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1e-5\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'accuracy'\u003c/span\u003e]\n)\n\n\u003cspan class=\"pl-c\"\u003e# Train your model\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003evalidation_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etest_dataset\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eepochs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e,\n)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\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\"\u003eIf you'd like to contribute, please see our \u003ca href=\"/keras-team/keras-cv/blob/master/CONTRIBUTING.md\"\u003econtributing guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo find an issue to tackle, please check our \u003ca href=\"/keras-team/keras-cv/blob/master/CALL_FOR_CONTRIBUTIONS.md\"\u003ecall for contributions\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe would like to leverage/outsource the Keras community not only for bug reporting,\nbut also for active development for feature delivery. To achieve this, here is the predefined\nprocess for how to contribute to this repository:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eContributors are always welcome to help us fix an issue, add tests, better documentation.\u003c/li\u003e\n\u003cli\u003eIf contributors would like to create a backbone, we usually require a pre-trained weight set\nwith the model for one dataset as the first PR, and a training script as a follow-up. The training script will preferably help us reproduce the results claimed from paper. The backbone should be generic but the training script can contain paper specific parameters such as learning rate schedules and weight decays. The training script will be used to produce leaderboard results.\nExceptions apply to large transformer-based models which are difficult to train. If this is the case,\ncontributors should let us know so the team can help in training the model or providing GCP resources.\u003c/li\u003e\n\u003cli\u003eIf contributors would like to create a meta arch, please try to be aligned with our roadmap and create a PR for design review to make sure the meta arch is modular.\u003c/li\u003e\n\u003cli\u003eIf contributors would like to create a new input formatting which is not in our roadmap for the next 6 months, e.g., keypoint, please create an issue and ask for a sponsor.\u003c/li\u003e\n\u003cli\u003eIf contributors would like to support a new task which is not in our roadmap for the next 6 months, e.g., 3D reconstruction, please create an issue and ask for a sponsor.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eThank you to all of our wonderful contributors!\u003c/p\u003e\n\u003ca href=\"https://github.com/keras-team/keras-cv/graphs/contributors\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/6bafe1f05137c092f2dd9fb884dd320226851b3cd88bbc62ce98ee181d460b0e/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d6b657261732d7465616d2f6b657261732d6376\" data-canonical-src=\"https://contrib.rocks/image?repo=keras-team/keras-cv\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePretrained Weights\u003c/h2\u003e\u003ca id=\"user-content-pretrained-weights\" class=\"anchor\" aria-label=\"Permalink: Pretrained Weights\" href=\"#pretrained-weights\"\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\"\u003eMany models in KerasCV come with pre-trained weights.\nWith the exception of StableDiffusion and the standard Vision Transformer, all of these weights are trained using Keras and\nKerasCV components and training scripts in this repository.\nWhile some models are not trained with the same parameters or preprocessing pipeline\nas defined in their original publications, the KerasCV team ensures strong numerical performance.\nPerformance metrics for the provided pre-trained weights can be found\nin the training history for each documented task.\nAn example of this can be found in the ImageNet classification training\n\u003ca href=\"/keras-team/keras-cv/blob/master/examples/training/classification/imagenet/training_history.json\"\u003ehistory for backbone models\u003c/a\u003e.\nAll results are reproducible using the training scripts in this repository.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHistorically, many models have been trained on image datasets rescaled via manually\ncrafted normalization schemes.\nThe most common variant of manually crafted normalization scheme is subtraction of the\nimagenet mean pixel followed by standard deviation normalization based on the imagenet\npixel standard deviation.\nThis scheme is an artifact of the days of manual feature engineering, but is no longer\nrequired to score state of the art scores using modern deep learning architectures.\nDue to this, KerasCV is standardized to operate on images that have been rescaled using\na simple \u003ccode\u003e1/255\u003c/code\u003e rescaling layer.\nThis can be seen in all KerasCV training pipelines and code examples.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustom Ops\u003c/h2\u003e\u003ca id=\"user-content-custom-ops\" class=\"anchor\" aria-label=\"Permalink: Custom Ops\" href=\"#custom-ops\"\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\"\u003eNote that in some of the 3D Object Detection layers, custom TF ops are used. The\nbinaries for these ops are not shipped in our PyPi package in order to keep our\nwheels pure-Python.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you'd like to use these custom ops, you can install from source using the\ninstructions below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstalling KerasCV with Custom Ops from Source\u003c/h3\u003e\u003ca id=\"user-content-installing-kerascv-with-custom-ops-from-source\" class=\"anchor\" aria-label=\"Permalink: Installing KerasCV with Custom Ops from Source\" href=\"#installing-kerascv-with-custom-ops-from-source\"\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\"\u003eInstalling custom ops from source requires the \u003ca href=\"https://bazel.build/\" rel=\"nofollow\"\u003eBazel\u003c/a\u003e build\nsystem (version \u0026gt;= 5.4.0). Steps to install Bazel can be \u003ca href=\"https://github.com/keras-team/keras/blob/v2.11.0/.devcontainer/Dockerfile#L21-L23\"\u003efound here\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/keras-team/keras-cv.git\ncd keras-cv\n\npython3 build_deps/configure.py\n\nbazel build build_pip_pkg\nexport BUILD_WITH_CUSTOM_OPS=true\nbazel-bin/build_pip_pkg wheels\n\npip install wheels/keras_cv-*.whl\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003egit clone https://github.com/keras-team/keras-cv.git\ncd keras-cv\n\npython3 build_deps/configure.py\n\nbazel build build_pip_pkg\nexport BUILD_WITH_CUSTOM_OPS=true\nbazel-bin/build_pip_pkg wheels\n\npip install wheels/keras_cv-*.whl\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote that GitHub actions exist to release KerasCV with custom ops, but are\ncurrently disabled. You can use these \u003ca href=\"https://github.com/keras-team/keras-cv/blob/master/.github/workflows/release.yml\"\u003eactions\u003c/a\u003e\nin your own fork to create wheels for Linux (manylinux2014), MacOS (both x86 and ARM),\nand Windows.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\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\"\u003eKerasCV provides access to pre-trained models via the \u003ccode\u003ekeras_cv.models\u003c/code\u003e API.\nThese pre-trained models are provided on an \"as is\" basis, without warranties\nor conditions of any kind.\nThe following underlying models are provided by third parties, and are subject to separate\nlicenses:\nStableDiffusion, Vision Transformer\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCiting KerasCV\u003c/h2\u003e\u003ca id=\"user-content-citing-kerascv\" class=\"anchor\" aria-label=\"Permalink: Citing KerasCV\" href=\"#citing-kerascv\"\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\"\u003eIf KerasCV helps your research, we appreciate your citations.\nHere is the BibTeX entry:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@misc{wood2022kerascv,\n title={KerasCV},\n author={Wood, Luke and Tan, Zhenyu and Stenbit, Ian and Bischof, Jonathan and Zhu, Scott and Chollet, Fran\\c{c}ois and Sreepathihalli, Divyashree and Sampath, Ramesh and others},\n year={2022},\n howpublished={\\url{https://github.com/keras-team/keras-cv}},\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@misc\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003ewood2022kerascv\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eKerasCV\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eWood, Luke and Tan, Zhenyu and Stenbit, Ian and Bischof, Jonathan and Zhu, Scott and Chollet, Fran\\c{c}ois and Sreepathihalli, Divyashree and Sampath, Ramesh and others\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2022\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ehowpublished\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e\\url{https://github.com/keras-team/keras-cv}\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n}\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"KerasCV","anchor":"kerascv","htmlText":"KerasCV"},{"level":2,"text":"Quick Links","anchor":"quick-links","htmlText":"Quick Links"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"Keras 2 Installation","anchor":"keras-2-installation","htmlText":"Keras 2 Installation"},{"level":3,"text":"Keras 3 Installation","anchor":"keras-3-installation","htmlText":"Keras 3 Installation"},{"level":2,"text":"Configuring your backend","anchor":"configuring-your-backend","htmlText":"Configuring your backend"},{"level":2,"text":"Quickstart","anchor":"quickstart","htmlText":"Quickstart"},{"level":2,"text":"Contributors","anchor":"contributors","htmlText":"Contributors"},{"level":2,"text":"Pretrained Weights","anchor":"pretrained-weights","htmlText":"Pretrained Weights"},{"level":2,"text":"Custom Ops","anchor":"custom-ops","htmlText":"Custom Ops"},{"level":3,"text":"Installing KerasCV with Custom Ops from Source","anchor":"installing-kerascv-with-custom-ops-from-source","htmlText":"Installing KerasCV with Custom Ops from Source"},{"level":2,"text":"Disclaimer","anchor":"disclaimer","htmlText":"Disclaimer"},{"level":2,"text":"Citing KerasCV","anchor":"citing-kerascv","htmlText":"Citing 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alt="Downloads" data-canonical-src="https://img.shields.io/pypi/dm/keras-cv.svg" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/049bd61fb4517a65d7a479bd4723fb8f3fa2913cc6f90893d5c9691f14202201/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d76332e372e302b2d737563636573732e737667"><img src="https://camo.githubusercontent.com/049bd61fb4517a65d7a479bd4723fb8f3fa2913cc6f90893d5c9691f14202201/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d76332e372e302b2d737563636573732e737667" alt="Python" data-canonical-src="https://img.shields.io/badge/python-v3.7.0+-success.svg" style="max-width: 100%;"></a></p> <div class="markdown-alert markdown-alert-important" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-report mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path></svg>Important</p><p dir="auto">📢 KerasCV is moving to <a href="https://github.com/keras-team/keras-hub/">KerasHub</a>! 📢 Read <a href="https://github.com/keras-team/keras-hub/issues/1831" data-hovercard-type="issue" data-hovercard-url="/keras-team/keras-hub/issues/1831/hovercard">the announcement</a>.</p> <p dir="auto">KerasCV will not continue to work as is; nothing will break. All development of new vision models should move to KerasHub.</p> </div> <p dir="auto">KerasCV is a library of modular computer vision components that work natively with TensorFlow, JAX, or PyTorch. Built on Keras 3, these models, layers, metrics, callbacks, etc., can be trained and serialized in any framework and re-used in another without costly migrations. See "Configuring your backend" below for more details on multi-framework KerasCV.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/b7b12717e6c1e89048f44901d03e5bf54ba43b7f0763a007a66fec258d45dfc3/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f6b657261732d63762f6775696465732f6b657261732d63762d6175676d656e746174696f6e732e676966"><img style="width: 440px; max-width: 100%;" src="https://camo.githubusercontent.com/b7b12717e6c1e89048f44901d03e5bf54ba43b7f0763a007a66fec258d45dfc3/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f6b657261732d63762f6775696465732f6b657261732d63762d6175676d656e746174696f6e732e676966" data-animated-image="" data-canonical-src="https://storage.googleapis.com/keras-cv/guides/keras-cv-augmentations.gif"></a></p> <p dir="auto">KerasCV can be understood as a horizontal extension of the Keras API: the components are new first-party Keras objects that are too specialized to be added to core Keras. They receive the same level of polish and backwards compatibility guarantees as the core Keras API, and they are maintained by the Keras team.</p> <p dir="auto">Our APIs assist in common computer vision tasks such as data augmentation, classification, object detection, segmentation, image generation, and more. Applied computer vision engineers can leverage KerasCV to quickly assemble production-grade, state-of-the-art training and inference pipelines for all of these common tasks.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quick Links</h2><a id="user-content-quick-links" class="anchor" aria-label="Permalink: Quick Links" href="#quick-links"><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><a href="https://keras.io/api/keras_cv/models/" rel="nofollow">List of available models and presets</a></li> <li><a href="https://keras.io/guides/keras_cv/" rel="nofollow">Developer Guides</a></li> <li><a href="/keras-team/keras-cv/blob/master/CONTRIBUTING.md">Contributing Guide</a></li> <li><a href="https://github.com/keras-team/keras-cv/issues?q=is%3Aopen+is%3Aissue+label%3Acontribution-welcome">Call for Contributions</a></li> <li><a href="/keras-team/keras-cv/blob/master/API_DESIGN.md">API Design Guidelines</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><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">KerasCV supports both Keras 2 and Keras 3. We recommend Keras 3 for all new users, as it enables using KerasCV models and layers with JAX, TensorFlow and PyTorch.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Keras 2 Installation</h3><a id="user-content-keras-2-installation" class="anchor" aria-label="Permalink: Keras 2 Installation" href="#keras-2-installation"><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">To install the latest KerasCV release with Keras 2, simply run:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pip install --upgrade keras-cv tensorflow"><pre class="notranslate"><code>pip install --upgrade keras-cv tensorflow </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Keras 3 Installation</h3><a id="user-content-keras-3-installation" class="anchor" aria-label="Permalink: Keras 3 Installation" href="#keras-3-installation"><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">There are currently two ways to install Keras 3 with KerasCV. To install the latest changes for KerasCV and Keras, you can use our nightly package.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pip install --upgrade keras-cv-nightly tf-nightly"><pre class="notranslate"><code>pip install --upgrade keras-cv-nightly tf-nightly </code></pre></div> <p dir="auto">To install the stable versions of KerasCV and Keras 3, you should install Keras 3 <strong>after</strong> installing KerasCV. This is a temporary step while TensorFlow is pinned to Keras 2, and will no longer be necessary after TensorFlow 2.16.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pip install --upgrade keras-cv tensorflow pip install --upgrade keras"><pre class="notranslate"><code>pip install --upgrade keras-cv tensorflow pip install --upgrade keras </code></pre></div> <div class="markdown-alert markdown-alert-important" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-report mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path></svg>Important</p><p dir="auto">Keras 3 will not function with TensorFlow 2.14 or earlier.</p> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Configuring your backend</h2><a id="user-content-configuring-your-backend" class="anchor" aria-label="Permalink: Configuring your backend" href="#configuring-your-backend"><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">If you have Keras 3 installed in your environment (see installation above), you can use KerasCV with any of JAX, TensorFlow and PyTorch. To do so, set the <code>KERAS_BACKEND</code> environment variable. For example: so by setting the <code>KERAS_BACKEND</code> environment variable. For example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export KERAS_BACKEND=jax"><pre><span class="pl-k">export</span> KERAS_BACKEND=jax</pre></div> <p dir="auto">Or in Colab, with:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import os os.environ["KERAS_BACKEND"] = "jax" import keras_cv"><pre><span class="pl-k">import</span> <span class="pl-s1">os</span> <span class="pl-s1">os</span>.<span class="pl-s1">environ</span>[<span class="pl-s">"KERAS_BACKEND"</span>] <span class="pl-c1">=</span> <span class="pl-s">"jax"</span> <span class="pl-k">import</span> <span class="pl-s1">keras_cv</span></pre></div> <div class="markdown-alert markdown-alert-important" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-report mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path></svg>Important</p><p dir="auto">Make sure to set the <code>KERAS_BACKEND</code> before import any Keras libraries, it will be used to set up Keras when it is first imported.</p> </div> <p dir="auto">Once that configuration step is done, you can just import KerasCV and start using it on top of your backend of choice:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import keras_cv import keras filepath = keras.utils.get_file(origin="https://i.imgur.com/gCNcJJI.jpg") image = np.array(keras.utils.load_img(filepath)) image_resized = keras.ops.image.resize(image, (640, 640))[None, ...] model = keras_cv.models.YOLOV8Detector.from_preset( "yolo_v8_m_pascalvoc", bounding_box_format="xywh", ) predictions = model.predict(image_resized)"><pre><span class="pl-k">import</span> <span class="pl-s1">keras_cv</span> <span class="pl-k">import</span> <span class="pl-s1">keras</span> <span class="pl-s1">filepath</span> <span class="pl-c1">=</span> <span class="pl-s1">keras</span>.<span class="pl-s1">utils</span>.<span class="pl-en">get_file</span>(<span class="pl-s1">origin</span><span class="pl-c1">=</span><span class="pl-s">"https://i.imgur.com/gCNcJJI.jpg"</span>) <span class="pl-s1">image</span> <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-en">array</span>(<span class="pl-s1">keras</span>.<span class="pl-s1">utils</span>.<span class="pl-en">load_img</span>(<span class="pl-s1">filepath</span>)) <span class="pl-s1">image_resized</span> <span class="pl-c1">=</span> <span class="pl-s1">keras</span>.<span class="pl-s1">ops</span>.<span class="pl-s1">image</span>.<span class="pl-en">resize</span>(<span class="pl-s1">image</span>, (<span class="pl-c1">640</span>, <span class="pl-c1">640</span>))[<span class="pl-c1">None</span>, ...] <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-s1">keras_cv</span>.<span class="pl-s1">models</span>.<span class="pl-v">YOLOV8Detector</span>.<span class="pl-en">from_preset</span>( <span class="pl-s">"yolo_v8_m_pascalvoc"</span>, <span class="pl-s1">bounding_box_format</span><span class="pl-c1">=</span><span class="pl-s">"xywh"</span>, ) <span class="pl-s1">predictions</span> <span class="pl-c1">=</span> <span class="pl-s1">model</span>.<span class="pl-en">predict</span>(<span class="pl-s1">image_resized</span>)</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quickstart</h2><a id="user-content-quickstart" class="anchor" aria-label="Permalink: Quickstart" href="#quickstart"><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="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import tensorflow as tf import keras_cv import tensorflow_datasets as tfds import keras # Create a preprocessing pipeline with augmentations BATCH_SIZE = 16 NUM_CLASSES = 3 augmenter = keras_cv.layers.Augmenter( [ keras_cv.layers.RandomFlip(), keras_cv.layers.RandAugment(value_range=(0, 255)), keras_cv.layers.CutMix(), ], ) def preprocess_data(images, labels, augment=False): labels = tf.one_hot(labels, NUM_CLASSES) inputs = {"images": images, "labels": labels} outputs = inputs if augment: outputs = augmenter(outputs) return outputs['images'], outputs['labels'] train_dataset, test_dataset = tfds.load( 'rock_paper_scissors', as_supervised=True, split=['train', 'test'], ) train_dataset = train_dataset.batch(BATCH_SIZE).map( lambda x, y: preprocess_data(x, y, augment=True), num_parallel_calls=tf.data.AUTOTUNE).prefetch( tf.data.AUTOTUNE) test_dataset = test_dataset.batch(BATCH_SIZE).map( preprocess_data, num_parallel_calls=tf.data.AUTOTUNE).prefetch( tf.data.AUTOTUNE) # Create a model using a pretrained backbone backbone = keras_cv.models.EfficientNetV2Backbone.from_preset( "efficientnetv2_b0_imagenet" ) model = keras_cv.models.ImageClassifier( backbone=backbone, num_classes=NUM_CLASSES, activation="softmax", ) model.compile( loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(learning_rate=1e-5), metrics=['accuracy'] ) # Train your model model.fit( train_dataset, validation_data=test_dataset, epochs=8, )"><pre><span class="pl-k">import</span> <span class="pl-s1">tensorflow</span> <span class="pl-k">as</span> <span class="pl-s1">tf</span> <span class="pl-k">import</span> <span class="pl-s1">keras_cv</span> <span class="pl-k">import</span> <span class="pl-s1">tensorflow_datasets</span> <span class="pl-k">as</span> <span class="pl-s1">tfds</span> <span class="pl-k">import</span> <span class="pl-s1">keras</span> <span class="pl-c"># Create a preprocessing pipeline with augmentations</span> <span class="pl-v">BATCH_SIZE</span> <span class="pl-c1">=</span> <span class="pl-c1">16</span> <span class="pl-v">NUM_CLASSES</span> <span class="pl-c1">=</span> <span class="pl-c1">3</span> <span class="pl-s1">augmenter</span> <span class="pl-c1">=</span> <span class="pl-s1">keras_cv</span>.<span class="pl-s1">layers</span>.<span class="pl-v">Augmenter</span>( [ <span class="pl-s1">keras_cv</span>.<span class="pl-s1">layers</span>.<span class="pl-v">RandomFlip</span>(), <span class="pl-s1">keras_cv</span>.<span class="pl-s1">layers</span>.<span class="pl-v">RandAugment</span>(<span class="pl-s1">value_range</span><span class="pl-c1">=</span>(<span class="pl-c1">0</span>, <span class="pl-c1">255</span>)), <span class="pl-s1">keras_cv</span>.<span class="pl-s1">layers</span>.<span class="pl-v">CutMix</span>(), ], ) <span class="pl-k">def</span> <span class="pl-en">preprocess_data</span>(<span class="pl-s1">images</span>, <span class="pl-s1">labels</span>, <span class="pl-s1">augment</span><span class="pl-c1">=</span><span class="pl-c1">False</span>): <span class="pl-s1">labels</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">one_hot</span>(<span class="pl-s1">labels</span>, <span class="pl-v">NUM_CLASSES</span>) <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> {<span class="pl-s">"images"</span>: <span class="pl-s1">images</span>, <span class="pl-s">"labels"</span>: <span class="pl-s1">labels</span>} <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-s1">inputs</span> <span class="pl-k">if</span> <span class="pl-s1">augment</span>: <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-en">augmenter</span>(<span class="pl-s1">outputs</span>) <span class="pl-k">return</span> <span class="pl-s1">outputs</span>[<span class="pl-s">'images'</span>], <span class="pl-s1">outputs</span>[<span class="pl-s">'labels'</span>] <span class="pl-s1">train_dataset</span>, <span class="pl-s1">test_dataset</span> <span class="pl-c1">=</span> <span class="pl-s1">tfds</span>.<span class="pl-en">load</span>( <span class="pl-s">'rock_paper_scissors'</span>, <span class="pl-s1">as_supervised</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">split</span><span class="pl-c1">=</span>[<span class="pl-s">'train'</span>, <span class="pl-s">'test'</span>], ) <span class="pl-s1">train_dataset</span> <span class="pl-c1">=</span> <span class="pl-s1">train_dataset</span>.<span class="pl-en">batch</span>(<span class="pl-v">BATCH_SIZE</span>).<span class="pl-en">map</span>( <span class="pl-k">lambda</span> <span class="pl-s1">x</span>, <span class="pl-s1">y</span>: <span class="pl-en">preprocess_data</span>(<span class="pl-s1">x</span>, <span class="pl-s1">y</span>, <span class="pl-s1">augment</span><span class="pl-c1">=</span><span class="pl-c1">True</span>), <span class="pl-s1">num_parallel_calls</span><span class="pl-c1">=</span><span class="pl-s1">tf</span>.<span class="pl-s1">data</span>.<span class="pl-v">AUTOTUNE</span>).<span class="pl-en">prefetch</span>( <span class="pl-s1">tf</span>.<span class="pl-s1">data</span>.<span class="pl-v">AUTOTUNE</span>) <span class="pl-s1">test_dataset</span> <span class="pl-c1">=</span> <span class="pl-s1">test_dataset</span>.<span class="pl-en">batch</span>(<span class="pl-v">BATCH_SIZE</span>).<span class="pl-en">map</span>( <span class="pl-s1">preprocess_data</span>, <span class="pl-s1">num_parallel_calls</span><span class="pl-c1">=</span><span class="pl-s1">tf</span>.<span class="pl-s1">data</span>.<span class="pl-v">AUTOTUNE</span>).<span class="pl-en">prefetch</span>( <span class="pl-s1">tf</span>.<span class="pl-s1">data</span>.<span class="pl-v">AUTOTUNE</span>) <span class="pl-c"># Create a model using a pretrained backbone</span> <span class="pl-s1">backbone</span> <span class="pl-c1">=</span> <span class="pl-s1">keras_cv</span>.<span class="pl-s1">models</span>.<span class="pl-v">EfficientNetV2Backbone</span>.<span class="pl-en">from_preset</span>( <span class="pl-s">"efficientnetv2_b0_imagenet"</span> ) <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-s1">keras_cv</span>.<span class="pl-s1">models</span>.<span class="pl-v">ImageClassifier</span>( <span class="pl-s1">backbone</span><span class="pl-c1">=</span><span class="pl-s1">backbone</span>, <span class="pl-s1">num_classes</span><span class="pl-c1">=</span><span class="pl-v">NUM_CLASSES</span>, <span class="pl-s1">activation</span><span class="pl-c1">=</span><span class="pl-s">"softmax"</span>, ) <span class="pl-s1">model</span>.<span class="pl-en">compile</span>( <span class="pl-s1">loss</span><span class="pl-c1">=</span><span class="pl-s">'categorical_crossentropy'</span>, <span class="pl-s1">optimizer</span><span class="pl-c1">=</span><span class="pl-s1">keras</span>.<span class="pl-s1">optimizers</span>.<span class="pl-v">Adam</span>(<span class="pl-s1">learning_rate</span><span class="pl-c1">=</span><span class="pl-c1">1e-5</span>), <span class="pl-s1">metrics</span><span class="pl-c1">=</span>[<span class="pl-s">'accuracy'</span>] ) <span class="pl-c"># Train your model</span> <span class="pl-s1">model</span>.<span class="pl-en">fit</span>( <span class="pl-s1">train_dataset</span>, <span class="pl-s1">validation_data</span><span class="pl-c1">=</span><span class="pl-s1">test_dataset</span>, <span class="pl-s1">epochs</span><span class="pl-c1">=</span><span class="pl-c1">8</span>, )</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributors</h2><a id="user-content-contributors" class="anchor" aria-label="Permalink: Contributors" href="#contributors"><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">If you'd like to contribute, please see our <a href="/keras-team/keras-cv/blob/master/CONTRIBUTING.md">contributing guide</a>.</p> <p dir="auto">To find an issue to tackle, please check our <a href="/keras-team/keras-cv/blob/master/CALL_FOR_CONTRIBUTIONS.md">call for contributions</a>.</p> <p dir="auto">We would like to leverage/outsource the Keras community not only for bug reporting, but also for active development for feature delivery. To achieve this, here is the predefined process for how to contribute to this repository:</p> <ol dir="auto"> <li>Contributors are always welcome to help us fix an issue, add tests, better documentation.</li> <li>If contributors would like to create a backbone, we usually require a pre-trained weight set with the model for one dataset as the first PR, and a training script as a follow-up. The training script will preferably help us reproduce the results claimed from paper. The backbone should be generic but the training script can contain paper specific parameters such as learning rate schedules and weight decays. The training script will be used to produce leaderboard results. Exceptions apply to large transformer-based models which are difficult to train. If this is the case, contributors should let us know so the team can help in training the model or providing GCP resources.</li> <li>If contributors would like to create a meta arch, please try to be aligned with our roadmap and create a PR for design review to make sure the meta arch is modular.</li> <li>If contributors would like to create a new input formatting which is not in our roadmap for the next 6 months, e.g., keypoint, please create an issue and ask for a sponsor.</li> <li>If contributors would like to support a new task which is not in our roadmap for the next 6 months, e.g., 3D reconstruction, please create an issue and ask for a sponsor.</li> </ol> <p dir="auto">Thank you to all of our wonderful contributors!</p> <a href="https://github.com/keras-team/keras-cv/graphs/contributors"> <img src="https://camo.githubusercontent.com/6bafe1f05137c092f2dd9fb884dd320226851b3cd88bbc62ce98ee181d460b0e/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d6b657261732d7465616d2f6b657261732d6376" data-canonical-src="https://contrib.rocks/image?repo=keras-team/keras-cv" style="max-width: 100%;"> </a> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Pretrained Weights</h2><a id="user-content-pretrained-weights" class="anchor" aria-label="Permalink: Pretrained Weights" href="#pretrained-weights"><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">Many models in KerasCV come with pre-trained weights. With the exception of StableDiffusion and the standard Vision Transformer, all of these weights are trained using Keras and KerasCV components and training scripts in this repository. While some models are not trained with the same parameters or preprocessing pipeline as defined in their original publications, the KerasCV team ensures strong numerical performance. Performance metrics for the provided pre-trained weights can be found in the training history for each documented task. An example of this can be found in the ImageNet classification training <a href="/keras-team/keras-cv/blob/master/examples/training/classification/imagenet/training_history.json">history for backbone models</a>. All results are reproducible using the training scripts in this repository.</p> <p dir="auto">Historically, many models have been trained on image datasets rescaled via manually crafted normalization schemes. The most common variant of manually crafted normalization scheme is subtraction of the imagenet mean pixel followed by standard deviation normalization based on the imagenet pixel standard deviation. This scheme is an artifact of the days of manual feature engineering, but is no longer required to score state of the art scores using modern deep learning architectures. Due to this, KerasCV is standardized to operate on images that have been rescaled using a simple <code>1/255</code> rescaling layer. This can be seen in all KerasCV training pipelines and code examples.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Custom Ops</h2><a id="user-content-custom-ops" class="anchor" aria-label="Permalink: Custom Ops" href="#custom-ops"><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">Note that in some of the 3D Object Detection layers, custom TF ops are used. The binaries for these ops are not shipped in our PyPi package in order to keep our wheels pure-Python.</p> <p dir="auto">If you'd like to use these custom ops, you can install from source using the instructions below.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Installing KerasCV with Custom Ops from Source</h3><a id="user-content-installing-kerascv-with-custom-ops-from-source" class="anchor" aria-label="Permalink: Installing KerasCV with Custom Ops from Source" href="#installing-kerascv-with-custom-ops-from-source"><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">Installing custom ops from source requires the <a href="https://bazel.build/" rel="nofollow">Bazel</a> build system (version >= 5.4.0). Steps to install Bazel can be <a href="https://github.com/keras-team/keras/blob/v2.11.0/.devcontainer/Dockerfile#L21-L23">found here</a>.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="git clone https://github.com/keras-team/keras-cv.git cd keras-cv python3 build_deps/configure.py bazel build build_pip_pkg export BUILD_WITH_CUSTOM_OPS=true bazel-bin/build_pip_pkg wheels pip install wheels/keras_cv-*.whl"><pre class="notranslate"><code>git clone https://github.com/keras-team/keras-cv.git cd keras-cv python3 build_deps/configure.py bazel build build_pip_pkg export BUILD_WITH_CUSTOM_OPS=true bazel-bin/build_pip_pkg wheels pip install wheels/keras_cv-*.whl </code></pre></div> <p dir="auto">Note that GitHub actions exist to release KerasCV with custom ops, but are currently disabled. You can use these <a href="https://github.com/keras-team/keras-cv/blob/master/.github/workflows/release.yml">actions</a> in your own fork to create wheels for Linux (manylinux2014), MacOS (both x86 and ARM), and Windows.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Disclaimer</h2><a id="user-content-disclaimer" class="anchor" aria-label="Permalink: Disclaimer" href="#disclaimer"><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">KerasCV provides access to pre-trained models via the <code>keras_cv.models</code> API. These pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind. The following underlying models are provided by third parties, and are subject to separate licenses: StableDiffusion, Vision Transformer</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citing KerasCV</h2><a id="user-content-citing-kerascv" class="anchor" aria-label="Permalink: Citing KerasCV" href="#citing-kerascv"><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">If KerasCV helps your research, we appreciate your citations. Here is the BibTeX entry:</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@misc{wood2022kerascv, title={KerasCV}, author={Wood, Luke and Tan, Zhenyu and Stenbit, Ian and Bischof, Jonathan and Zhu, Scott and Chollet, Fran\c{c}ois and Sreepathihalli, Divyashree and Sampath, Ramesh and others}, year={2022}, howpublished={\url{https://github.com/keras-team/keras-cv}}, }"><pre><span class="pl-k">@misc</span>{<span class="pl-en">wood2022kerascv</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>KerasCV<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Wood, Luke and Tan, Zhenyu and Stenbit, Ian and Bischof, Jonathan and Zhu, Scott and Chollet, Fran\c{c}ois and Sreepathihalli, Divyashree and Sampath, Ramesh and others<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2022<span class="pl-pds">}</span></span>, <span class="pl-s">howpublished</span>=<span class="pl-s"><span class="pl-pds">{</span>\url{https://github.com/keras-team/keras-cv}<span class="pl-pds">}</span></span>, }</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="/axX9Fu2TAyDlrr39SVJgKclYSZt2lsb2zei1mgIgpttGqwzXXYFEwXa/42sxM+1yrb+edWTc06X6b5XLk5RQQ==" /> </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"> Industry-strength Computer Vision workflows with Keras </p> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" 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1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.006.005-.01.01-.045.04c-.21.176-.441.327-.686.45C14.556 10.78 13.88 11 13 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L12.178 4.5h-.162c-.305 0-.604-.079-.868-.231l-1.29-.736a.245.245 0 0 0-.124-.033H8.75V13h2.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1 0-1.5h2.5V3.5h-.984a.245.245 0 0 0-.124.033l-1.289.737c-.265.15-.564.23-.869.23h-.162l2.112 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.016.015-.045.04c-.21.176-.441.327-.686.45C4.556 10.78 3.88 11 3 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L2.178 4.5H1.75a.75.75 0 0 1 0-1.5h2.234a.249.249 0 0 0 .125-.033l1.288-.737c.265-.15.564-.23.869-.23h.984V.75a.75.75 0 0 1 1.5 0Zm2.945 8.477c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327Zm-10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327Z"></path> </svg> Security policy </a> </div> <include-fragment src="/keras-team/keras-cv/hovercards/citation/sidebar_partial?tree_name=master"> </include-fragment> <div class="mt-2"> <a href="/keras-team/keras-cv/activity" data-view-component="true" class="Link Link--muted"> <svg text="gray" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-pulse mr-2"> <path d="M6 2c.306 0 .582.187.696.471L10 10.731l1.304-3.26A.751.751 0 0 1 12 7h3.25a.75.75 0 0 1 0 1.5h-2.742l-1.812 4.528a.751.751 0 0 1-1.392 0L6 4.77 4.696 8.03A.75.75 0 0 1 4 8.5H.75a.75.75 0 0 1 0-1.5h2.742l1.812-4.529A.751.751 0 0 1 6 2Z"></path> </svg> <span class="color-fg-muted">Activity</span> </a> </div> <div class="mt-2"> <a href="/keras-team/keras-cv/custom-properties" data-view-component="true" class="Link Link--muted"> <svg text="gray" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-note mr-2"> <path d="M0 3.75C0 2.784.784 2 1.75 2h12.5c.966 0 1.75.784 1.75 1.75v8.5A1.75 1.75 0 0 1 14.25 14H1.75A1.75 1.75 0 0 1 0 12.25Zm1.75-.25a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-8.5a.25.25 0 0 0-.25-.25ZM3.5 6.25a.75.75 0 0 1 .75-.75h7a.75.75 0 0 1 0 1.5h-7a.75.75 0 0 1-.75-.75Zm.75 2.25h4a.75.75 0 0 1 0 1.5h-4a.75.75 0 0 1 0-1.5Z"></path> </svg> <span class="color-fg-muted">Custom properties</span> </a> </div> <h3 class="sr-only">Stars</h3> <div class="mt-2"> <a href="/keras-team/keras-cv/stargazers" 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-star mr-2"> <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> <strong>1k</strong> stars </a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/keras-team/keras-cv/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 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>31</strong> watching </a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/keras-team/keras-cv/forks" 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-repo-forked mr-2"> <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> <strong>330</strong> forks </a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fkeras-team%2Fkeras-cv&report=keras-team+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/keras-team/keras-cv/releases" data-view-component="true" class="Link--primary no-underline Link"> Releases <span title="46" data-view-component="true" class="Counter">46</span> </a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/keras-team/keras-cv/releases/tag/v0.9.0"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <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 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">v0.9.0</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2024-04-30T19:54:24Z" class="no-wrap">Apr 30, 2024</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/keras-team/keras-cv/releases" data-view-component="true" class="Link"> + 45 releases </a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/keras-team/packages?repo_name=keras-cv" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center"> Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span> </a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" > <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/keras-team/keras-cv/network/dependents" data-view-component="true" class="Link--primary no-underline Link"> Used by <span title="611" data-view-component="true" class="Counter">611</span> </a> </h2> <a class="d-flex flex-items-center" href="/keras-team/keras-cv/network/dependents"> <ul class="hx_flex-avatar-stack list-style-none min-width-0"> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/112618768?s=64&v=4" width="32" height="32" alt="@lamnguyenthien" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/126926699?s=64&v=4" width="32" height="32" alt="@GitXpresso" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/164009725?s=64&v=4" width="32" height="32" alt="@HolyTostadora" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/26126457?s=64&v=4" width="32" height="32" alt="@HansSanchez" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/43724325?s=64&v=4" width="32" height="32" alt="@alessiomora" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/86916708?s=64&v=4" width="32" height="32" alt="@i-Shreyansh" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/88677587?s=64&v=4" width="32" height="32" alt="@arijitbhowal" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/110008411?s=64&v=4" width="32" height="32" alt="@feroz-05" /> </li> </ul> <span class="px-2 text-bold text-small no-wrap"> + 603 </span> </a> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/keras-team/keras-cv/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center"> Contributors <span title="99" data-view-component="true" class="Counter ml-1">99</span> </a></h2> <include-fragment src="/keras-team/keras-cv/contributors_list?count=99&current_repository=keras-cv&items_to_show=14" aria-busy="true" aria-label="Loading contributors"> <ul class="list-style-none d-flex flex-wrap mb-n2"> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> </ul> </include-fragment> <div data-view-component="true" class="mt-3"> <a text="small" href="/keras-team/keras-cv/graphs/contributors" data-view-component="true" class="Link--inTextBlock Link"> + 85 contributors </a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#3572A5 !important;;width: 84.1%;" itemprop="keywords" aria-label="Python 84.1" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#DA5B0B !important;;width: 14.4%;" itemprop="keywords" aria-label="Jupyter Notebook 14.4" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#f34b7d !important;;width: 0.9%;" itemprop="keywords" aria-label="C++ 0.9" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#89e051 !important;;width: 0.3%;" itemprop="keywords" aria-label="Shell 0.3" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#76d275 !important;;width: 0.3%;" itemprop="keywords" aria-label="Starlark 0.3" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#f0c040 !important;;width: 0.0%;" itemprop="keywords" aria-label="Smarty 0.0" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/keras-team/keras-cv/search?l=python" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>84.1%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/keras-team/keras-cv/search?l=jupyter-notebook" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#DA5B0B;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Jupyter Notebook</span> <span>14.4%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/keras-team/keras-cv/search?l=c%2B%2B" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f34b7d;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C++</span> <span>0.9%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/keras-team/keras-cv/search?l=shell" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#89e051;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Shell</span> <span>0.3%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/keras-team/keras-cv/search?l=starlark" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#76d275;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Starlark</span> <span>0.3%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/keras-team/keras-cv/search?l=smarty" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f0c040;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Smarty</span> <span>0.0%</span> </a> </li> </ul> </div> </div> </div> </div> </div></div> </div> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex flex-justify-center flex-items-center flex-column-reverse flex-lg-row flex-wrap flex-lg-nowrap"> <div class="d-flex 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