https://github.com/Layout-Parser/layout-model-training

The scripts for training Detectron2-based Layout Models on popular layout analysis datasets
https://github.com/Layout-Parser/layout-model-training

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

• Host: GitHub
• URL: https://github.com/Layout-Parser/layout-model-training
• Owner: Layout-Parser
• Created: 2020-07-07T20:44:41.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-09-26T07:22:34.000Z (about 1 year ago)
• Last Synced: 2024-11-11T01:12:28.821Z (1 day ago)
• Language: Python
• Size: 23.4 KB
• Stars: 202
• Watchers: 9
• Forks: 55
• Open Issues: 16
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://github.com/Layout-Parser/layout-parser/tree/main/examples/Customizing%20Layout%20Models%20with%20Label%20Studio%20Annotation). 

- Duplicate and modify the config files and training scripts

    - For example, you might want to copy [`configs/prima/fast_rcnn_R_50_FPN_3x`](configs/prima/fast_rcnn_R_50_FPN_3x.yaml) to [`configs/your-dataset-name/fast_rcnn_R_50_FPN_3x`](configs/prima/fast_rcnn_R_50_FPN_3x.yaml), and you can create your own `scripts/train_.sh` based on [`scripts/train_prima.sh`](scripts/train_prima.sh).

    - You'll modify the `--dataset_name`, `--json_annotation_train`, `--image_path_train`, `--json_annotation_val`, `--image_path_val`, and `--config-file` args appropriately. 

- If you have a dataset with segmentation masks, you can try to train with the [`mask_rcnn model`](configs/prima/mask_rcnn_R_50_FPN_3x.yaml); otherwise you might want to start with the [`fast_rcnn model`](configs/prima/fast_rcnn_R_50_FPN_3x.yaml)

    - 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`](https://github.com/Layout-Parser/layout-model-training/blob/master/scripts/train_prima.sh)

    - You will need to download the dataset from the [official website](https://www.primaresearch.org/dataset/) and put it in the `data/prima` folder. 

    - As the original dataset is stored in the [PAGE format](https://www.primaresearch.org/tools/PAGEViewer), the script will use [`tools/convert_prima_to_coco.py`](https://github.com/Layout-Parser/layout-model-training/blob/master/tools/convert_prima_to_coco.py) to convert it to COCO format. 

    - The final dataset folder structure should look like:

        ```bash

        data/

        └── prima/

            ├── Images/

            ├── XML/

            ├── License.txt

            └── annotations*.json

        ```

## Reference 

- **[cocosplit](https://github.com/akarazniewicz/cocosplit)**  A script that splits the coco annotations into train and test sets.

- **[Detectron2](https://github.com/facebookresearch/detectron2)** Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms.