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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

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