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The scripts for training Detectron2-based Layout Models on popular layout analysis datasets

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Scripts for training Layout Detection Models using Detectron2

Usage

Directory Structure

  • In tools/, we provide a series of handy scripts for converting data formats and training the models.
  • In scripts/, it lists specific command for running the code for processing the given dataset.
  • The configs/ contains the configuration for different deep learning models, and is organized by datasets.

How to train the models?

  • Get the dataset and annotations -- if you are not sure, feel free to check this tutorial.
  • Duplicate and modify the config files and training scripts
  • If you have a dataset with segmentation masks, you can try to train with the mask_rcnn model; otherwise you might want to start with the fast_rcnn model
    • If you see error AttributeError: Cannot find field 'gt_masks' in the given Instances! during training, this means you should not use

Supported Datasets

  • Prima Layout Analysis Dataset scripts/train_prima.sh
    • You will need to download the dataset from the official website and put it in the data/prima folder.
    • As the original dataset is stored in the PAGE format, the script will use tools/convert_prima_to_coco.py to convert it to COCO format.
    • The final dataset folder structure should look like:
      data/
      └── prima/
          ├── Images/
          ├── XML/
          ├── License.txt
          └── annotations*.json

Reference

  • cocosplit A script that splits the coco annotations into train and test sets.
  • Detectron2 Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms.

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