metadata
license: apache-2.0
pipeline_tag: image-to-text
tags:
- layout
anydoclayout
docs layout detection
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/ _ \ | '_ \| | | | | | |/ _ \ / __| | / _` | | | |/ _ \| | | | __|
/ ___ \| | | | |_| | |_| | (_) | (__| |__| (_| | |_| | (_) | |_| | |_
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- Github：anydoclayout
- Hugging Face: anydoclayout
- ModelScope: anydoclayout
train datasets
1. labels info
{0: 'header',
1: 'title',
2: 'text',
3: 'table',
4: 'figure',
5: 'formula',
6: 'footer',
7: 'pagenum'}
2. datasets info
- train: 841862 (opendata: 667426, selfgen: 174436)
- eval: 5500
- imgsize：1280
3. eval results
Class Images Instances Box(P R)
all 5500 52274 0.921 0.897
header 1461 2337 0.92 0.878
title 2308 5473 0.896 0.893
text 4149 34156 0.937 0.927
table 1476 1913 0.946 0.958
figure 1842 3343 0.94 0.94
formula 735 1506 0.881 0.876
footer 745 1157 0.909 0.781
pagenum 2164 2389 0.938 0.919
if you want to get datasets
- email：christnowx@qq.com
how to use
from pathlib import Path
from ultralytics import YOLO
modelfile = Path(model_dir).joinpath('anydoclayout-yolo11s-imgsz1280.pt')
model = YOLO(modelfile)
res = model.predict('your img file', imgsz = 1280)
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