lquirosd / RPN_DLA

RPN approach to DLA problem

RPN_DLA

RPN approach to DLA problem

Requirements

• Linux (OSX may work, but untested.).
• Python (3.7 under conda virtual environment is recomended)
• Pytorch (1.5.1)
• Detectron2

Installation

1. Install python dependencies using requirements file
2. Install detectron2

Usage

1. Input data must follow the folder structure data_tag/page, where images must be into the data_tag folder and xml files into page. For example:

mkdir -p data/{train,val,test,prod}/page;
tree data;

data
├── prod
│   ├── page
│   │   ├── prod_0.xml
│   │   └── prod_1.xml
│   ├── prod_0.jpg
│   └── prod_1.jpg
├── test
│   ├── page
│   │   ├── test_0.xml
│   │   └── test_1.xml
│   ├── test_0.jpg
│   └── test_1.jpg
├── train
│   ├── page
│   │   ├── train_0.xml
│   │   └── train_1.xml
│   ├── train_0.jpg
│   └── train_1.jpg
└── val
    ├── page
    │   ├── val_0.xml
    │   └── val_1.xml
    ├── val_0.jpg
    └── val_1.jpg

2. Convert PAGE-XML data into COCO-JSON

python utils/page2coco.py --page_dir <> --include <> --classes <> --output <>

3. Run the tool

python train_net.py --num-gpus <1> --config-file <configs/> SOLVER.IMS_PER_BATCH <4> SOLVER.BASE_LR <0.01>

An example config file can be found in configs folder

4. Convert results fron JSON to PAGE-XML

python utils/detectronCoco2page.py --results_json <> --dataset_json <> --output <>

5. Gen the baselines if required

python utils/baselines/baselines.py --img_dir <> --page_dir <> --out_dir <>

License

Apache 2.0 license. See LICENSE to see the full text.