BUDDI-AI/cell-detection-CDeCNet
Introduction
license: apache-2.0 ---
language: en ---
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: