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    2025-03-28T03:26:14+00:00
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    DocLayout-YOLO-DocStructBench is a document layout detection model developed by the Shanghai AI Lab, based on the YOLO-v10 framework. It is designed to perform real-time and robust detection of various document layouts through diverse document pre-training and structural optimization. The model uses the Mesh-candidate BestFit algorithm and the DocSynth-300K dataset to enhance its fine-tuning performance across different document types.

    0
    2025-03-28T03:26:23+00:00
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    DocLayout-YOLO-DocStructBench employs the Mesh-candidate BestFit algorithm to generate the DocSynth-300K dataset, which is used for diverse document pre-training. The dataset, which is 113G in size, significantly improves the model's performance during fine-tuning. Additionally, the model includes the Global-to-Local Controllable Receptive Module to handle multi-scale document elements, enhancing detection accuracy.

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    2025-03-28T03:26:33+00:00
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    The key features of DocLayout-YOLO-DocStructBench include:
    - Diverse document pre-training using the Mesh-candidate BestFit algorithm and the DocSynth-300K dataset.
    - Structural optimization with the Global-to-Local Controllable Receptive Module for better handling of multi-scale document elements.
    - Real-time performance, achieving high mAP scores on datasets like D4LA, DocLayNet, and DocStructBench, while maintaining an inference speed of 85.5 FPS.

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    2025-03-28T03:26:45+00:00
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    The main functions of DocLayout-YOLO-DocStructBench include:
    - Real-time layout detection of various document types, suitable for document understanding systems.
    - Multi-modal support, enhancing the detection of text, images, and tables through pre-training data.
    - Performance validation on complex benchmarks like DocStructBench, demonstrating its applicability in diverse document scenarios.

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