Instructions to use moon-g/DocLayout-YOLO-DocStructBench-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- YOLOv10
How to use moon-g/DocLayout-YOLO-DocStructBench-onnx with YOLOv10:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("moon-g/DocLayout-YOLO-DocStructBench-onnx") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
DocLayout-YOLO-DocStructBench ONNX(moon-reader 镜像)
moon-reader(小月亮阅读器)使用的 DocLayout-YOLO 版面识别模型 ONNX 镜像。
来源
由 wybxc/DocLayout-YOLO-DocStructBench-onnx 转存,原模型转换自 juliozhao/DocLayout-YOLO-DocStructBench(Apache-2.0)。
本仓库为 moon-reader 应用内模型下载的稳定镜像源,避免上游仓库变动影响应用。
文件
| 文件 | 大小 | SHA256 |
|---|---|---|
doclayout_yolo_docstructbench_imgsz1024.onnx |
75,324,598 B | fece9af02f618b603ff7921ccec6861d13e7e1f9830e091dfb7e8ad9311e5b21 |
模型规格
- 架构:Ultralytics YOLOv10m
- 任务:版面检测(10 类)
- 输入:
[1, 3, 1024, 1024]f32,RGB→BGR 转换 + letterbox(pad 114)+ 归一化/255 - 输出:
[1, 300, 6](x1, y1, x2, y2, score, class_id),端到端 NMS
类别顺序(class id)
0: title
1: plain text
2: abandon
3: figure
4: figure_caption
5: table
6: table_caption
7: table_footnote
8: isolate_formula
9: formula_caption
使用
应用内自动下载(国内源优先,SHA256 校验)。推理用 tract(纯 Rust)或 ONNX Runtime。
- Downloads last month
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Model tree for moon-g/DocLayout-YOLO-DocStructBench-onnx
Base model
juliozhao/DocLayout-YOLO-DocStructBench