| --- |
| license: apache-2.0 |
| language: |
| - en |
| metrics: |
| - precision |
| - recall |
| base_model: |
| - Ultralytics/YOLOv8 |
| pipeline_tag: object-detection |
| --- |
| |
| # YOLO Document Layout Model |
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| This model is a fine-tuned YOLO detector for document layout analysis, capable of identifying various document elements such as text columns, figures, tables, and other typographical features. |
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| ## Interactive Demo |
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| Try the model directly in your browser: |
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| [](https://ashen007-yolo-document-layout-demo.hf.space/?__theme=system) |
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| ## Model Description |
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| The model is trained to detect and classify 20 different document components, including text structures (TextColumn, List), semantic elements (Title, Header), typographical features (Bold, Italic), and visual components (Figure, Table). |
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| ## Model Detections |
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| ### Training |
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| The model was fine-tuned using a proprietary dataset of document images. |
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| ## Evaluation Results |
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| The model's performance was evaluated on a test set with the following metrics: |
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| | Class | Images | Instances | Precision | Recall | mAP50 | mAP50-95 | |
| |-------|--------|-----------|-----------|--------|-------|----------| |
| | **all** | **150** | **1255** | **0.701** | **0.723** | **0.735** | **0.509** | |
| | Author | 7 | 65 | 0.693 | 0.174 | 0.307 | 0.134 | |
| | Bigletter | 11 | 11 | 1.000 | 0.900 | 0.976 | 0.563 | |
| | Bleeding | 9 | 10 | 0.618 | 0.700 | 0.667 | 0.547 | |
| | Bold | 23 | 77 | 0.679 | 0.753 | 0.798 | 0.395 | |
| | Caption | 50 | 71 | 0.892 | 0.816 | 0.881 | 0.642 | |
| | Date | 17 | 57 | 0.927 | 0.666 | 0.728 | 0.386 | |
| | Figure | 90 | 149 | 0.772 | 0.725 | 0.823 | 0.677 | |
| | Footnote | 14 | 15 | 0.500 | 0.667 | 0.612 | 0.478 | |
| | Header | 16 | 16 | 0.560 | 0.717 | 0.664 | 0.476 | |
| | Italic | 17 | 86 | 0.448 | 0.791 | 0.557 | 0.327 | |
| | List | 34 | 55 | 0.615 | 0.709 | 0.742 | 0.591 | |
| | Map | 4 | 4 | 0.606 | 0.750 | 0.656 | 0.599 | |
| | SubSubTitle | 37 | 97 | 0.627 | 0.520 | 0.599 | 0.300 | |
| | SubTitle | 54 | 96 | 0.605 | 0.562 | 0.605 | 0.327 | |
| | Table | 30 | 43 | 0.865 | 0.953 | 0.966 | 0.855 | |
| | TextColumn | 115 | 323 | 0.831 | 0.913 | 0.933 | 0.811 | |
| | Title | 47 | 66 | 0.712 | 0.711 | 0.649 | 0.441 | |
| | Underline | 2 | 4 | 0.681 | 1.000 | 0.995 | 0.665 | |
| | equations | 4 | 10 | 0.688 | 0.700 | 0.809 | 0.450 | |
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| ### Key Performance Highlights: |
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| - **Best performing classes**: Table (mAP50: 0.966), TextColumn (mAP50: 0.933), and Caption (mAP50: 0.881) |
| - **High precision classes**: Bigletter (1.000), Date (0.927), and Caption (0.892) |
| - **High recall classes**: Underline (1.000), Table (0.953), and TextColumn (0.913) |
| - **Overall performance**: mAP50 of 0.735 and mAP50-95 of 0.509 across all classes |
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| ## Limitations |
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| - Lower performance on Author detection (mAP50: 0.307) |
| - Moderate performance on typographical features like Italic (mAP50: 0.557) |
| - Limited sample size for some classes (Map, Underline, equations) |
