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# Introduction
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license: apache-2.0
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language: en
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## BUDDI Table Factory: A toolbox for generating synthetic documents with annotated tables and cells
**About**
In Cell 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 | 10 |
| Step Interval |50 |
***Model Paper Reference***
CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images
https://arxiv.org/abs/2008.10831
### Citation
If you find BTF useful for your work, please cite the following paper:
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