| .. | ||
| .gitkeep | ||
| config_params.json | ||
| config_params_docker.json | ||
| custom_config_page2label.json | ||
| Dockerfile | ||
| README.md | ||
| requirements.txt | ||
| scales_enhancement.json | ||
Training eynollah
This README explains the technical details of how to set up and run training, for detailed information on parameterization, see docs/train.md
Introduction
This folder contains the source code for training an encoder model for document image segmentation.
Installation
Clone the repository and install eynollah along with the dependencies necessary for training:
git clone https://github.com/qurator-spk/eynollah
cd eynollah
pip install '.[training]'
Pretrained encoder
Download our pretrained weights and add them to a train/pretrained_model folder:
cd train
wget -O pretrained_model.tar.gz https://zenodo.org/records/17243320/files/pretrained_model_v0_5_1.tar.gz?download=1
tar xf pretrained_model.tar.gz
Binarization training data
A small sample of training data for binarization experiment can be found on
zenodo,
which contains images and labels folders.
Helpful tools
Tool to extract 2-D or 3-D RGB images from PAGE-XML data. In the former case, the output will be 1 2-D image array which each class has filled with a pixel value. In the case of a 3-D RGB image, each class will be defined with a RGB value and beside images, a text file of classes will also be produced.
Convert COCO GT or results for a single image to a segmentation map and write it to disk.
Extract region classes and their colours in mask (pseg) images. Allows the color map as free dict parameter, and comes with a default that mimics PageViewer's coloring for quick debugging; it also warns when regions do overlap.
Train using Docker
Build the Docker image:
cd train
docker build -t model-training .
Run Docker image
cd train
docker run --gpus all -v $PWD:/entry_point_dir model-training