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--- |
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license: apache-2.0 |
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pipeline_tag: object-detection |
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tags: |
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- table-detect |
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- table-recong |
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--- |
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# AnyTable |
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<a href="https://huggingface.co/anyforge/anytable" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97-HuggingFace-blue"></a> |
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<a href="https://www.modelscope.cn/models/anyforge/anytable" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/%E9%AD%94%E6%90%AD-ModelScope-blue"></a> |
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<a href=""><img src="https://img.shields.io/badge/Python->=3.6-aff.svg"></a> |
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<a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a> |
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<a href=""><img alt="Static Badge" src="https://img.shields.io/badge/engine-cpu_gpu_onnxruntime-blue"></a> |
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``` |
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___ ______ __ __ |
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/ | ____ __ _/_ __/___ _/ /_ / /__ |
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/ /| | / __ \/ / / // / / __ `/ __ \/ / _ \ |
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/ ___ |/ / / / /_/ // / / /_/ / /_/ / / __/ |
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/_/ |_/_/ /_/\__, //_/ \__,_/_.___/_/\___/ |
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/____/ |
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``` |
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简体中文 | [English](./README_en.md) |
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<div align="left"> |
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<img src="./assets/sample1.jpg"> |
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</div> |
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## 1. 简介 |
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AnyTable是一个专注于从文档或者图片中表格解析的模型工具，主要分成两个部分： |
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- anytable-det：用于表格区域检测（已开放） |
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- anytable-rec：用于表格结构识别（未来开放） |
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项目地址： |
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- github地址：[AnyTable](https://github.com/anyforge/anytable) |
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- Hugging Face: [AnyTable](https://huggingface.co/anyforge/anytable) |
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- ModelScope: [AnyTable](https://www.modelscope.cn/models/anyforge/anytable) |
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## 2. 缘起 |
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目前市面上表格数据非常多且混杂，很难有一个干净的完整数据和模型，为此我们收集并整理了很多表格数据，训练了我们的模型。 |
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检测数据集分布： |
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- pubtables: 947642 |
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- synthtabnet.marketing: 149999 |
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- tablebank: 278582 |
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- fintabnet.c: 97475 |
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- pubtabnet: 519030 |
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- synthtabnet.sparse: 150000 |
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- synthtabnet.fintabnet: 149999 |
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- docbank: 24517 |
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- synthtabnet.pubtabnet: 150000 |
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- cTDaRTRACKA: 1639 |
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- SciTSR: 14971 |
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- doclaynet.large: 21185 |
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- IIITAR13K: 9905 |
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- selfbuilt: 121157 |
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数据集总计：大于`2.6M`(大约2633869张图片)。 |
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### 扩展训练 |
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- 训练集：`2.6M（大于10万的部分只抽样了42000， 没办法因为贫穷，卡有限。）` |
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- 测试集：`4k` |
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- python: 3.12 |
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- pytorch: 2.6.0 |
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- cuda: 12.3 |
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- ultralytics: 8.3.128 |
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### 模型介绍 |
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表格检测模型位于det文件夹下： |
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- yolo系列：使用ultralytics训练yolo检测 |
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- rt-detr：使用ultralytics训练rt-detr检测 |
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注释：您可以直接模型预测，也可以作为预训练模型微调私有数据集 |
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### 评估 |
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自建评估集：`4K` |
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| model | imgsz | epochs | metrics/precision | |
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|---|---|---|---| |
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|rt-detr-l|960|10|0.97| |
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|yolo11s|960|10|0.97| |
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|yolo11m|960|10|0.964| |
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|yolo12s|960|10|0.978| |
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## 3. 使用方法 |
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### 安装依赖 |
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```bash |
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pip install ultralytics pillow |
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``` |
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### 使用方法 |
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```python |
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## simple |
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## 下载模型后直接使用ultralytics即可 |
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from ultralytics import YOLO,RTDETR |
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# Load a model |
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model = YOLO("/path/to/download_model") # pretrained YOLO11n model |
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# Run batched inference on a list of images |
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results = model(["/path/to/your_image"],imgsz = 960) # return a list of Results objects |
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# Process results list |
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for result in results: |
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boxes = result.boxes # Boxes object for bounding box outputs |
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masks = result.masks # Masks object for segmentation masks outputs |
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keypoints = result.keypoints # Keypoints object for pose outputs |
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probs = result.probs # Probs object for classification outputs |
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obb = result.obb # Oriented boxes object for OBB outputs |
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result.show() # display to screen |
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result.save(filename="result.jpg") # save to disk |
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``` |
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## Buy me a coffee |
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- 微信(WeChat) |
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<div align="left"> |
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<img src="./zanshan.jpg" width="30%" height="30%"> |
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</div> |
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## 特别鸣谢 |
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- ultralytics公开的训练模型和文档 |
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- 各种数据集提供者 |
