# AnyTable
```
___ ______ __ __
/ | ____ __ _/_ __/___ _/ /_ / /__
/ /| | / __ \/ / / // / / __ `/ __ \/ / _ \
/ ___ |/ / / / /_/ // / / /_/ / /_/ / / __/
/_/ |_/_/ /_/\__, //_/ \__,_/_.___/_/\___/
/____/
```
English | [简体中文](./README.md)
## 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/anyforge/anytable)
- Hugging Face: [AnyTable](https://huggingface.co/anyforge/anytable)
- ModelScope: [AnyTable](https://www.modelscope.cn/models/anyforge/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|0.97|
|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)
## Special thanks
- Ultralytics publicly available training models and documentation
- Various dataset providers
