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README.md

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----
-license: apache-2.0
----
+# AnyTable
+
+<a href="https://huggingface.co/oriforge/anytable" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97-HuggingFace-blue"></a>
+<a href="https://www.modelscope.cn/models/oriforge/table" 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/oriforge/anytable)
+- Hugging Face: [AnyTable](https://huggingface.co/oriforge/anytable)
+- ModelScope: [AnyTable](https://www.modelscope.cn/models/oriforge/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|-|
+|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

README_en.md

@@ -0,0 +1,134 @@
+# AnyTable
+
+<a href="https://huggingface.co/oriforge/anytable" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97-HuggingFace-blue"></a>
+<a href="https://www.modelscope.cn/models/oriforge/table" 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>
+
+```
+    ___               ______      __    __   
+   /   |  ____  __  _/_  __/___ _/ /_  / /__ 
+  / /| | / __ \/ / / // / / __ `/ __ \/ / _ \
+ / ___ |/ / / / /_/ // / / /_/ / /_/ / /  __/
+/_/  |_/_/ /_/\__, //_/  \__,_/_.___/_/\___/ 
+             /____/                          
+
+```
+
+English | [简体中文](./README.md)
+
+<div align="left">
+    <img src="./assets/sample1.jpg">
+</div>
+
+## 1. Introduction
+
+AnyTable is a modeling tool that focuses on parsing tables from documents or images, mainly divided into two parts:
+-Anytable det: used for table region detection (open)
+-Anytable rec: used for table structure recognition (open in the future)
+
+Project Address:
+- github地址：[AnyTable](https://github.com/oriforge/anytable)
+- Hugging Face: [AnyTable](https://huggingface.co/oriforge/anytable)
+- ModelScope: [AnyTable](https://www.modelscope.cn/models/oriforge/anytable)
+
+## 2. Origin
+
+At present, there are a lot of mixed table data on the market, making it difficult to have a clean and complete data and model. Therefore, we collected and organized a lot of table data and trained our model.
+
+Detecting dataset distribution:
+
+- 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
+
+Total dataset: greater than 2.6M (approximately 2633869 images).
+
+### Train
+
+- train set：`2.6M(Only 42000 samples were taken for the portion greater than 100000，Due to poverty, the cards are limited.)`
+- eval set：`4k`
+- python: 3.12
+- pytorch: 2.6.0
+- cuda: 12.3
+- ultralytics: 8.3.128
+
+### Model introduction
+
+The table detection model is located in the det folder:
+
+- YOLO series: Training YOLO detection using ultralytics
+- Rt detr: Training rt detr detection using ultralytics
+
+Note: You can directly predict the model or fine tune the private dataset as a pre trained model
+
+### Eval
+
+self built evaluation set：`4K`
+
+| model | imgsz | epochs | metrics/precision |
+|---|---|---|---|
+|rt-detr-l|960|10|0.97|
+|yolo11s|960|10|-|
+|yolo11m|960|10|0.964|
+|yolo12s|960|10|0.978|
+
+
+## 3. Usage
+
+### Install dependencies
+
+```bash
+pip install ultralytics pillow
+```
+
+### Usage
+
+```python
+## simple
+## After downloading the model, simply use ultralytics directly
+
+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>
+
+## Special thanks
+
+- Ultralytics publicly available training models and documentation
+- Various dataset providers

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