PubTables-1M

• Quick Start:

• Pre-trained weights: Link

Update:

• Mar 14 2022:
  • Release pre-trained weights (20 epochs on full training set)
• Mar 5 2022:
  • Release pre-trained weights (12 epochs on full training set)
• Mar 1 2022:
  • Release pre-trained weights (7 epochs on full training set)
  • Update core.py and google colab for new code
• Jan 23 2022:
  • Release pre-trained weights (20 epochs on a small set of data)
• Jan 17 2022:
  • Release pre-trained weights (11 epochs on a small set of data).
  • Add docker training
  • Add streamlit

Evaluation of pre-trained epoches

Epoch 20 - training on full training set

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.912
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.970
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.947
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.709
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.910
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.916
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.445
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.861
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.941
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.763
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.938
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.947
pubmed: AP50: 0.970, AP75: 0.947, AP: 0.912, AR: 0.941
Total training time:  18 days, 15:03:14.605917

For Docker users

docker pull phamquiluan/table-transformer:latest
# or
docker build -t phamquiluan/table-transformer -f Dockerfile .

# train TSR
docker run -it --shm-size 8G --gpus all -v /data/pubtables1m/PubTables1M-Structure-PASCAL-VOC:/code/data phamquiluan/table-transformer /bin/bash
cd src; python3 main.py --data_root_dir /code/data --data_type structure

Code Installation

Create a virtual environment and activate it as follows

python3.7 -m venv env; source env/bin/activate
pip install -U pip

pip install -r requirements.txt

For streamlit users

streamlit run app.py

Original README