AnyTable
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English | 简体中文
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:
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
pip install ultralytics pillow
Usage
## 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