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*Floatings* are common in academic literature and formal publications. In LaTeX, floating objects refer to containers that can include text, images, tables, code, algorithms, etc. Current Document Layout Analysis (DLA) models tend to handle such elements in a relatively crude and superficial manner, making it difficult to perform fine-grained layout analysis. To address this issue, models from the YOLO11 series (with five types: n, s, m, l, x) and the RT-DETR series (types l and x) have been trained using the Ultralytics framework on the Floating Layout Detection (FLD) and Floating Structure Analysis (FSA) datasets. These models are capable of automatically detecting and analyzing floating objects in document images. Additionally, the models' weights and training parameters have been made publicly available to facilitate collaborative research.
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# 下载方式
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## Download Methods
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You can download the models either via Git or `huggingface-cli`.
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*Floatings* are common in academic literature and formal publications. In LaTeX, floating objects refer to containers that can include text, images, tables, code, algorithms, etc. Current Document Layout Analysis (DLA) models tend to handle such elements in a relatively crude and superficial manner, making it difficult to perform fine-grained layout analysis. To address this issue, models from the YOLO11 series (with five types: n, s, m, l, x) and the RT-DETR series (types l and x) have been trained using the Ultralytics framework on the Floating Layout Detection (FLD) and Floating Structure Analysis (FSA) datasets. These models are capable of automatically detecting and analyzing floating objects in document images. Additionally, the models' weights and training parameters have been made publicly available to facilitate collaborative research.
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## Download Methods
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You can download the models either via Git or `huggingface-cli`.
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