| --- |
| license: apache-2.0 |
| pipeline_tag: object-detection |
| tags: |
| - table-detect |
| - table-recong |
| --- |
| # AnyTable |
|
|
| <a href="https://huggingface.co/anyforge/anytable" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97-HuggingFace-blue"></a> |
| <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> |
| <a href=""><img src="https://img.shields.io/badge/Python->=3.6-aff.svg"></a> |
| <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a> |
| <a href=""><img alt="Static Badge" src="https://img.shields.io/badge/engine-cpu_gpu_onnxruntime-blue"></a> |
|
|
| ``` |
| ___ ______ __ __ |
| / | ____ __ _/_ __/___ _/ /_ / /__ |
| / /| | / __ \/ / / // / / __ `/ __ \/ / _ \ |
| / ___ |/ / / / /_/ // / / /_/ / /_/ / / __/ |
| /_/ |_/_/ /_/\__, //_/ \__,_/_.___/_/\___/ |
| /____/ |
| |
| ``` |
|
|
| 简体中文 | [English](./README_en.md) |
|
|
| <div align="left"> |
| <img src="./assets/sample1.jpg"> |
| </div> |
| |
|
|
| ## 1. 简介 |
|
|
| 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： |
| - anytable-det：用于表格区域检测（已开放） |
| - anytable-rec：用于表格结构识别（未来开放） |
|
|
| 项目地址： |
| - github地址：[AnyTable](https://github.com/anyforge/anytable) |
| - Hugging Face: [AnyTable](https://huggingface.co/anyforge/anytable) |
| - ModelScope: [AnyTable](https://www.modelscope.cn/models/anyforge/anytable) |
|
|
| ## 2. 缘起 |
|
|
| 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。 |
|
|
| 检测数据集分布： |
|
|
| - pubtables: 947642 |
| - synthtabnet.marketing: 149999 |
| - tablebank: 278582 |
| - fintabnet.c: 97475 |
| - pubtabnet: 519030 |
| - synthtabnet.sparse: 150000 |
| - synthtabnet.fintabnet: 149999 |
| - docbank: 24517 |
| - synthtabnet.pubtabnet: 150000 |
| - cTDaRTRACKA: 1639 |
| - SciTSR: 14971 |
| - doclaynet.large: 21185 |
| - IIITAR13K: 9905 |
| - selfbuilt: 121157 |
|
|
| 数据集总计：大于`2.6M`(大约2633869张图片)。 |
|
|
| ### 扩展训练 |
|
|
| - 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。）` |
| - 测试集：`4k` |
| - python: 3.12 |
| - pytorch: 2.6.0 |
| - cuda: 12.3 |
| - ultralytics: 8.3.128 |
|
|
| ### 模型介绍 |
|
|
| 表格检测模型位于det文件夹下： |
| - yolo系列：使用ultralytics训练yolo检测 |
| - rt-detr：使用ultralytics训练rt-detr检测 |
|
|
| 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 |
|
|
| ### 评估 |
|
|
| 自建评估集：`4K` |
|
|
| | model | imgsz | epochs | metrics/precision | |
| |---|---|---|---| |
| |rt-detr-l|960|10|0.97| |
| |yolo11s|960|10|0.97| |
| |yolo11m|960|10|0.964| |
| |yolo12s|960|10|0.978| |
|
|
|
|
| ## 3. 使用方法 |
|
|
| ### 安装依赖 |
|
|
| ```bash |
| pip install ultralytics pillow |
| ``` |
|
|
| ### 使用方法 |
|
|
| ```python |
| ## simple |
| ## 下载模型后直接使用ultralytics即可 |
| |
| from ultralytics import YOLO,RTDETR |
| |
| # Load a model |
| model = YOLO("/path/to/download_model") # pretrained YOLO11n model |
| |
| # Run batched inference on a list of images |
| results = model(["/path/to/your_image"],imgsz = 960) # return a list of Results objects |
| |
| # Process results list |
| for result in results: |
| boxes = result.boxes # Boxes object for bounding box outputs |
| masks = result.masks # Masks object for segmentation masks outputs |
| keypoints = result.keypoints # Keypoints object for pose outputs |
| probs = result.probs # Probs object for classification outputs |
| obb = result.obb # Oriented boxes object for OBB outputs |
| result.show() # display to screen |
| result.save(filename="result.jpg") # save to disk |
| |
| ``` |
|
|
| ## Buy me a coffee |
|
|
| - 微信(WeChat) |
|
|
| <div align="left"> |
| <img src="./zanshan.jpg" width="30%" height="30%"> |
| </div> |
| |
| ## 特别鸣谢 |
| - ultralytics公开的训练模型和文档 |
| - 各种数据集提供者 |
