readme: ## BUDDI Table Factory: A toolbox for generating synthetic documents with annotated tables and cells
  
  
  **About**
  
  In table detection, we initialize the weights with a pre-trained CDeCNet model using COCO dataset. We re-train the model for five epochs using a stochastic gradient descent optimizer with a learning rate of 0.00125, the momentum of 0.9, and weight decay of 0.0001.
  
  ***Hardware Used***
  
  We perform all the experiments on NVIDIA GeForce RTX 2080 Ti GPU with 12 GB GPU memory, Intel(R) Xeon(R) CPU E5-2640 v2 @ 2.00GHz, and 128 GB of RAM.
  
  **Table Detection Model & Training Parameter**
  ***Optimizer*** 
  | Parameter |Value  |
  |--|--|
  | Type | SGD |
  | Learning Rate |0.00125  |
  | Momentum | 0.8 |
  | Weight Decay |0.001  |
  
  
  *** Learning Policy ***
  | Parameter |Value  |
  |--|--|
  | Policy | Step |
  |Warmup | Linear  |
  | Warmup Iteration | 100 |
  | Warmup Ratio |0.001  |
  | Step  | 4,16,32  |
  
  ***General Parameter***
  | Parameter |Value  |
  |--|--|
  | Epoch | 5 |
  | Step Interval |50  |
          
  ***Model Paper Reference***
  
  CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images
  
  https://arxiv.org/abs/2008.10831