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 huggingface_hub import hf_hub_download from ultralytics import YOLOv10 # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("moon-g/DocLayout-YOLO-DocStructBench-onnx", "<weights>.pt") model = YOLOv10(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,495 Bytes
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license: apache-2.0
base_model:
- juliozhao/DocLayout-YOLO-DocStructBench
language:
- en
pipeline_tag: object-detection
tags:
- layout-analysis
- document-understanding
- yolov10
- onnx
---
# 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](https://huggingface.co/wybxc/DocLayout-YOLO-DocStructBench-onnx) 转存,原模型转换自 [juliozhao/DocLayout-YOLO-DocStructBench](https://huggingface.co/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。
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