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- W4308234037 abstract "In recent years, several companies and researchers have started to tackle the problem of damage recognition within the scope of automated inspection of built structures. While companies are neither willing to publish associated data nor models, researchers are facing the problem of data shortage on one hand and inconsistent dataset splitting with the absence of consistent metrics on the other hand. This leads to incomparable results. Therefore, we introduce the building inspection toolkit – bikit – which acts as a simple to use data hub containing relevant open-source datasets in the field of damage recognition. The datasets are enriched with evaluation splits and predefined metrics, suiting the specific task and their data distribution. For the sake of compatibility and to motivate researchers in this domain, we also provide a leaderboard and the possibility to share model weights with the community. As a starting point we provide strong baselines utilizing extensive hyperparameter search using three transfer learning approaches for state-of-the-art algorithms. The toolkit <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> and the leaderboard <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> are available online." @default.
- W4308234037 created "2022-11-09" @default.
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- W4308234037 date "2022-10-16" @default.
- W4308234037 modified "2023-10-06" @default.
- W4308234037 title "Building Inspection Toolkit: Unified Evaluation And Strong Baselines For Bridge Damage Recognition" @default.
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- W4308234037 doi "https://doi.org/10.1109/icip46576.2022.9897743" @default.
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