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--- |
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license: mit |
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base_model: |
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- Ultralytics/YOLOv8 |
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pipeline_tag: object-detection |
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--- |
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# Overview |
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This repository hosts a YOLOv8l model trained on the ArxivFormula (https://github.com/microsoft/ArxivFormula) dataset, which focuses on the detection of mathematical expressions in scientific papers. |
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# Training Data: |
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- Source: ArxivFormula (https://github.com/microsoft/ArxivFormula) |
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- Classes: 6 classes (InlineFormula, DisplayedFormulaLine, FormulaNumber, DisplayedFormulaBlock, Table, Figure) |
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Pages: ~600,000 images of document pages |
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# Model: |
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- YOLOv8l (https://github.com/ultralytics/ultralytics) |
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- epochs = 100 |
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- imgsz = 640 |
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- optimizer = 'AdamW' |
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- lr0 = 0.0001 |
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- augment = True |
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# Usage |
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## Example Code |
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``` |
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from ultralytics import YOLO |
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import pathlib |
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# Sample images |
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img_list = ['sample1.png', 'sample2.png', 'sample3.png'] |
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# Load the document segmentation model |
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model = YOLO('arxivFormula_YOLOv8l.pt') |
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# Process the images |
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results = model(source=img_list, save=True, show_labels=True, show_conf=True, show_boxes=True) |
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``` |
