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GitHub - gmberton/VPR-methods-evaluation: Easily download and evaluate pre-trained Visual Place Recognition methods. Code built for the ICCV 2023 paper "EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition"
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</div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" 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class=\"heading-element\" dir=\"auto\"\u003eVPR-methods-evaluation\u003c/h1\u003e\u003ca id=\"user-content-vpr-methods-evaluation\" class=\"anchor\" aria-label=\"Permalink: VPR-methods-evaluation\" href=\"#vpr-methods-evaluation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repo allows you to easily test almost any SOTA VPR model within a minute.\nThe architecture code and weights are from the respective authors of the papers, ensuring reliability.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBasic use on an unlabelled dataset\u003c/h2\u003e\u003ca id=\"user-content-basic-use-on-an-unlabelled-dataset\" class=\"anchor\" aria-label=\"Permalink: Basic use on an unlabelled dataset\" href=\"#basic-use-on-an-unlabelled-dataset\"\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\"\u003eSimply run this to try a method on a (unlabelled) toy dataset contained in assets. This is super lightweight and will take a few seconds even running on a CPU of a laptop.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"git clone --recursive https://github.com/gmberton/VPR-methods-evaluation\ncd VPR-methods-evaluation\npython3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \\\n --database_folder=toy_dataset/database --queries_folder=toy_dataset/queries \\\n --no_labels --image_size 200 200 --num_preds_to_save 3 \\\n --log_dir toy_experiment\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003egit clone --recursive https://github.com/gmberton/VPR-methods-evaluation\ncd VPR-methods-evaluation\npython3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \\\n --database_folder=toy_dataset/database --queries_folder=toy_dataset/queries \\\n --no_labels --image_size 200 200 --num_preds_to_save 3 \\\n --log_dir toy_experiment\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ewhich will generate images like this within the \u003ccode\u003elogs/toy_experiment\u003c/code\u003e visual predictions for each query like this one\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/gmberton/VPR-methods-evaluation/master/images/no_labels_pred.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/gmberton/VPR-methods-evaluation/master/images/no_labels_pred.jpg\" height=\"200\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can also use this on your own dataset (or any two directories) simply changing the paths parameters.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBasic use on a labelled dataset\u003c/h2\u003e\u003ca id=\"user-content-basic-use-on-a-labelled-dataset\" class=\"anchor\" aria-label=\"Permalink: Basic use on a labelled dataset\" href=\"#basic-use-on-a-labelled-dataset\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe code is designed to be readily used with our \u003ca href=\"https://github.com/gmberton/VPR-datasets-downloader\"\u003eVPR-datasets-downloader\u003c/a\u003e repo, so that using a few simple commands you can download a dataset and test any model on it. The VPR-datasets-downloader code allows you to download multiple VPR datasets that are automatically formatted in the same format as used by this repo.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe format needed by this repo to compute recalls is that each image should use as filename \u003ccode\u003epath/to/image/@UTM_east@UTM_north@whatever@.jpg\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"mkdir VPR-codebase\ncd VPR-codebase\n\ngit clone https://github.com/gmberton/VPR-datasets-downloader\ncd VPR-datasets-downloader\npython3 download_st_lucia.py\n\ncd ..\n\ngit clone --recursive https://github.com/gmberton/VPR-methods-evaluation\ncd VPR-methods-evaluation\npython3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \\\n --database_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/database \\\n --queries_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/queries\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003emkdir VPR-codebase\ncd VPR-codebase\n\ngit clone https://github.com/gmberton/VPR-datasets-downloader\ncd VPR-datasets-downloader\npython3 download_st_lucia.py\n\ncd ..\n\ngit clone --recursive https://github.com/gmberton/VPR-methods-evaluation\ncd VPR-methods-evaluation\npython3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \\\n --database_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/database \\\n --queries_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/queries\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis should produce this as output \u003ccode\u003eR@1: 98.8, R@5: 99.7, R@10: 99.9, R@20: 100.0\u003c/code\u003e, which will be saved in a log file under \u003ccode\u003e./logs/\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can easily change the paths for different datasets, and you can use any supported method.\nNote that each method has weights only for certain architectures. For example NetVLAD only has weights for VGG16 with descriptors_dimension 32768 and 4069 (with PCA).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNB: make sure to use the \u003ccode\u003egit clone --recursive\u003c/code\u003e, otherwise some third party (like AP-GeM) models can't be used.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVisualize predictions\u003c/h3\u003e\u003ca id=\"user-content-visualize-predictions\" class=\"anchor\" aria-label=\"Permalink: Visualize predictions\" href=\"#visualize-predictions\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePredictions can be easily visualized through the \u003ccode\u003enum_preds_to_save\u003c/code\u003e parameter. For example running this\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \\\n --num_preds_to_save=3 --log_dir=cosplace_on_stlucia \\\n --database_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/database \\\n --queries_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/queries\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \\\n --num_preds_to_save=3 --log_dir=cosplace_on_stlucia \\\n --database_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/database \\\n --queries_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/queries\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ewill generate under the path \u003ccode\u003e./logs/cosplace_on_stlucia/*/preds\u003c/code\u003e images such as\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/gmberton/VPR-methods-evaluation/master/images/pred.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/gmberton/VPR-methods-evaluation/master/images/pred.jpg\" height=\"200\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eGiven that saving predictions for each query might take long, you can also pass the parameter \u003ccode\u003e--save_only_wrong_preds\u003c/code\u003e which will save only predictions for wrongly predicted queries (i.e. where the first prediction is wrong).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSupported models\u003c/h2\u003e\u003ca id=\"user-content-supported-models\" class=\"anchor\" aria-label=\"Permalink: Supported models\" href=\"#supported-models\"\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\"\u003eNetVLAD, AP-GeM, SFRS, CosPlace, Conv-AP, MixVPR, EigenPlaces, AnyLoc, SALAD, EigenPlaces-indoor, SALAD-indoor, CricaVPR, CliqueMining.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUnsupported models / contributing\u003c/h3\u003e\u003ca id=\"user-content-unsupported-models--contributing\" class=\"anchor\" aria-label=\"Permalink: Unsupported models / contributing\" href=\"#unsupported-models--contributing\"\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 some models that we tried to add but couldn't get to work, mostly due to issues in their codebases, namely VLAD-BuFF, Bag-of-Queries, DINO-Mix.\nWe'd gladly accept PRs from anyone who can get them to work.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo get a model to work simply add it to \u003ccode\u003eparser.py\u003c/code\u003e and add it to \u003ccode\u003evpr_models/__init__.py\u003c/code\u003e: if the model is easy to download (e.g. through \u003ccode\u003etorch.hub.load\u003c/code\u003e), adding a few (4-5) lines of code should be enough to make it work. There is no need to test it on a VPR dataset, just make it run on the toy dataset and then we'll test it on the VPR datasets ourselves to ensure correctness.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgements / Cite / BibTex\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements--cite--bibtex\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements / Cite / BibTex\" href=\"#acknowledgements--cite--bibtex\"\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 use this repository please cite our \u003ca href=\"https://openaccess.thecvf.com/content/ICCV2023/html/Berton_EigenPlaces_Training_Viewpoint_Robust_Models_for_Visual_Place_Recognition_ICCV_2023_paper.html\" rel=\"nofollow\"\u003eICCV 2023 EigenPlaces paper\u003c/a\u003e, for which we started this repo to ensure fair comparisons between VPR models:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{Berton_2023_EigenPlaces,\n title={EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition},\n author={Berton, Gabriele and Trivigno, Gabriele and Caputo, Barbara and Masone, Carlo},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},\n year={2023},\n month={October},\n pages={11080-11090}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{Berton_2023_EigenPlaces,\n title={EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition},\n author={Berton, Gabriele and Trivigno, Gabriele and Caputo, Barbara and Masone, Carlo},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},\n year={2023},\n month={October},\n pages={11080-11090}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eKudos to the authors of \u003ca href=\"https://github.com/Relja/netvlad\"\u003eNetVLAD\u003c/a\u003e, \u003ca href=\"https://github.com/naver/deep-image-retrieval\"\u003eAP-GeM\u003c/a\u003e, \u003ca href=\"https://github.com/yxgeee/OpenIBL\"\u003eSFRS\u003c/a\u003e, \u003ca href=\"https://github.com/gmberton/CosPlace\"\u003eCosPlace\u003c/a\u003e, \u003ca href=\"https://github.com/amaralibey/gsv-cities\"\u003eConv-AP\u003c/a\u003e, \u003ca href=\"https://github.com/amaralibey/mixVPR\"\u003eMixVPR\u003c/a\u003e, \u003ca href=\"https://github.com/gmberton/EigenPlaces\"\u003eEigenPlaces\u003c/a\u003e, \u003ca href=\"https://github.com/AnyLoc/AnyLoc\"\u003eAnyLoc\u003c/a\u003e, \u003ca href=\"https://github.com/serizba/salad\"\u003eSALAD\u003c/a\u003e, \u003ca href=\"https://github.com/Enrico-Chiavassa/Indoor-VPR\"\u003eEigenPlaces-indoor and SALAD-indoor\u003c/a\u003e for open sourcing their models' weights. The code for each model has been taken from their respective repositories, except for the code for NetVLAD which has been taken from \u003ca href=\"https://github.com/cvg/Hierarchical-Localization\"\u003ehloc\u003c/a\u003e.\nMake sure to cite them if you use each model's code.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"VPR-methods-evaluation","anchor":"vpr-methods-evaluation","htmlText":"VPR-methods-evaluation"},{"level":2,"text":"Basic use on an unlabelled dataset","anchor":"basic-use-on-an-unlabelled-dataset","htmlText":"Basic use on an unlabelled dataset"},{"level":2,"text":"Basic use on a labelled dataset","anchor":"basic-use-on-a-labelled-dataset","htmlText":"Basic use on a labelled dataset"},{"level":3,"text":"Visualize predictions","anchor":"visualize-predictions","htmlText":"Visualize predictions"},{"level":2,"text":"Supported models","anchor":"supported-models","htmlText":"Supported models"},{"level":3,"text":"Unsupported models / contributing","anchor":"unsupported-models--contributing","htmlText":"Unsupported models / contributing"},{"level":2,"text":"Acknowledgements / Cite / BibTex","anchor":"acknowledgements--cite--bibtex","htmlText":"Acknowledgements / Cite / BibTex"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fgmberton%2FVPR-methods-evaluation"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-9f8a877aa99f.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"accessible_code_button":true}}}}</script> <div data-target="react-partial.reactRoot"><style data-styled="true" 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aria-hidden="true" focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">VPR-methods-evaluation</h1><a id="user-content-vpr-methods-evaluation" class="anchor" aria-label="Permalink: VPR-methods-evaluation" href="#vpr-methods-evaluation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repo allows you to easily test almost any SOTA VPR model within a minute. The architecture code and weights are from the respective authors of the papers, ensuring reliability.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Basic use on an unlabelled dataset</h2><a id="user-content-basic-use-on-an-unlabelled-dataset" class="anchor" aria-label="Permalink: Basic use on an unlabelled dataset" href="#basic-use-on-an-unlabelled-dataset"><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">Simply run this to try a method on a (unlabelled) toy dataset contained in assets. This is super lightweight and will take a few seconds even running on a CPU of a laptop.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="git clone --recursive https://github.com/gmberton/VPR-methods-evaluation cd VPR-methods-evaluation python3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \ --database_folder=toy_dataset/database --queries_folder=toy_dataset/queries \ --no_labels --image_size 200 200 --num_preds_to_save 3 \ --log_dir toy_experiment"><pre class="notranslate"><code>git clone --recursive https://github.com/gmberton/VPR-methods-evaluation cd VPR-methods-evaluation python3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \ --database_folder=toy_dataset/database --queries_folder=toy_dataset/queries \ --no_labels --image_size 200 200 --num_preds_to_save 3 \ --log_dir toy_experiment </code></pre></div> <p dir="auto">which will generate images like this within the <code>logs/toy_experiment</code> visual predictions for each query like this one</p> <p dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/gmberton/VPR-methods-evaluation/master/images/no_labels_pred.jpg"><img src="https://raw.githubusercontent.com/gmberton/VPR-methods-evaluation/master/images/no_labels_pred.jpg" height="200" style="max-width: 100%;"></a> </p> <p dir="auto">You can also use this on your own dataset (or any two directories) simply changing the paths parameters.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Basic use on a labelled dataset</h2><a id="user-content-basic-use-on-a-labelled-dataset" class="anchor" aria-label="Permalink: Basic use on a labelled dataset" href="#basic-use-on-a-labelled-dataset"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The code is designed to be readily used with our <a href="https://github.com/gmberton/VPR-datasets-downloader">VPR-datasets-downloader</a> repo, so that using a few simple commands you can download a dataset and test any model on it. The VPR-datasets-downloader code allows you to download multiple VPR datasets that are automatically formatted in the same format as used by this repo.</p> <p dir="auto">The format needed by this repo to compute recalls is that each image should use as filename <code>path/to/image/@UTM_east@UTM_north@whatever@.jpg</code></p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="mkdir VPR-codebase cd VPR-codebase git clone https://github.com/gmberton/VPR-datasets-downloader cd VPR-datasets-downloader python3 download_st_lucia.py cd .. git clone --recursive https://github.com/gmberton/VPR-methods-evaluation cd VPR-methods-evaluation python3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \ --database_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/database \ --queries_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/queries"><pre class="notranslate"><code>mkdir VPR-codebase cd VPR-codebase git clone https://github.com/gmberton/VPR-datasets-downloader cd VPR-datasets-downloader python3 download_st_lucia.py cd .. git clone --recursive https://github.com/gmberton/VPR-methods-evaluation cd VPR-methods-evaluation python3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \ --database_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/database \ --queries_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/queries </code></pre></div> <p dir="auto">This should produce this as output <code>R@1: 98.8, R@5: 99.7, R@10: 99.9, R@20: 100.0</code>, which will be saved in a log file under <code>./logs/</code></p> <p dir="auto">You can easily change the paths for different datasets, and you can use any supported method. Note that each method has weights only for certain architectures. For example NetVLAD only has weights for VGG16 with descriptors_dimension 32768 and 4069 (with PCA).</p> <p dir="auto">NB: make sure to use the <code>git clone --recursive</code>, otherwise some third party (like AP-GeM) models can't be used.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Visualize predictions</h3><a id="user-content-visualize-predictions" class="anchor" aria-label="Permalink: Visualize predictions" href="#visualize-predictions"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Predictions can be easily visualized through the <code>num_preds_to_save</code> parameter. For example running this</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \ --num_preds_to_save=3 --log_dir=cosplace_on_stlucia \ --database_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/database \ --queries_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/queries"><pre class="notranslate"><code>python3 main.py --method=cosplace --backbone=ResNet18 --descriptors_dimension=512 \ --num_preds_to_save=3 --log_dir=cosplace_on_stlucia \ --database_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/database \ --queries_folder=../VPR-datasets-downloader/datasets/st_lucia/images/test/queries </code></pre></div> <p dir="auto">will generate under the path <code>./logs/cosplace_on_stlucia/*/preds</code> images such as</p> <p dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/gmberton/VPR-methods-evaluation/master/images/pred.jpg"><img src="https://raw.githubusercontent.com/gmberton/VPR-methods-evaluation/master/images/pred.jpg" height="200" style="max-width: 100%;"></a> </p> <p dir="auto">Given that saving predictions for each query might take long, you can also pass the parameter <code>--save_only_wrong_preds</code> which will save only predictions for wrongly predicted queries (i.e. where the first prediction is wrong).</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Supported models</h2><a id="user-content-supported-models" class="anchor" aria-label="Permalink: Supported models" href="#supported-models"><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">NetVLAD, AP-GeM, SFRS, CosPlace, Conv-AP, MixVPR, EigenPlaces, AnyLoc, SALAD, EigenPlaces-indoor, SALAD-indoor, CricaVPR, CliqueMining.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Unsupported models / contributing</h3><a id="user-content-unsupported-models--contributing" class="anchor" aria-label="Permalink: Unsupported models / contributing" href="#unsupported-models--contributing"><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 some models that we tried to add but couldn't get to work, mostly due to issues in their codebases, namely VLAD-BuFF, Bag-of-Queries, DINO-Mix. We'd gladly accept PRs from anyone who can get them to work.</p> <p dir="auto">To get a model to work simply add it to <code>parser.py</code> and add it to <code>vpr_models/__init__.py</code>: if the model is easy to download (e.g. through <code>torch.hub.load</code>), adding a few (4-5) lines of code should be enough to make it work. There is no need to test it on a VPR dataset, just make it run on the toy dataset and then we'll test it on the VPR datasets ourselves to ensure correctness.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgements / Cite / BibTex</h2><a id="user-content-acknowledgements--cite--bibtex" class="anchor" aria-label="Permalink: Acknowledgements / Cite / BibTex" href="#acknowledgements--cite--bibtex"><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 use this repository please cite our <a href="https://openaccess.thecvf.com/content/ICCV2023/html/Berton_EigenPlaces_Training_Viewpoint_Robust_Models_for_Visual_Place_Recognition_ICCV_2023_paper.html" rel="nofollow">ICCV 2023 EigenPlaces paper</a>, for which we started this repo to ensure fair comparisons between VPR models:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{Berton_2023_EigenPlaces, title={EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition}, author={Berton, Gabriele and Trivigno, Gabriele and Caputo, Barbara and Masone, Carlo}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, year={2023}, month={October}, pages={11080-11090} }"><pre class="notranslate"><code>@inproceedings{Berton_2023_EigenPlaces, title={EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition}, author={Berton, Gabriele and Trivigno, Gabriele and Caputo, Barbara and Masone, Carlo}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, year={2023}, month={October}, pages={11080-11090} } </code></pre></div> <p dir="auto">Kudos to the authors of <a href="https://github.com/Relja/netvlad">NetVLAD</a>, <a href="https://github.com/naver/deep-image-retrieval">AP-GeM</a>, <a href="https://github.com/yxgeee/OpenIBL">SFRS</a>, <a href="https://github.com/gmberton/CosPlace">CosPlace</a>, <a href="https://github.com/amaralibey/gsv-cities">Conv-AP</a>, <a href="https://github.com/amaralibey/mixVPR">MixVPR</a>, <a href="https://github.com/gmberton/EigenPlaces">EigenPlaces</a>, <a href="https://github.com/AnyLoc/AnyLoc">AnyLoc</a>, <a href="https://github.com/serizba/salad">SALAD</a>, <a href="https://github.com/Enrico-Chiavassa/Indoor-VPR">EigenPlaces-indoor and SALAD-indoor</a> for open sourcing their models' weights. The code for each model has been taken from their respective repositories, except for the code for NetVLAD which has been taken from <a href="https://github.com/cvg/Hierarchical-Localization">hloc</a>. Make sure to cite them if you use each model's code.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="9ehEmQD2N+5xINNN9cztT4O2reh+C2BHS1oZUop9LDwBvuYv5CSu8qfymXTltFTyFT5bLG0wvj85v5iydSHSbA==" /> </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"> Easily download and evaluate pre-trained Visual Place Recognition methods. 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