Machine Learning, NLP

Detectron2 for Document Layout Analysis

Detectron2

This repo contains the training configurations, code and trained models trained on PubLayNet dataset using Detectron2 implementation.
PubLayNet is a very large dataset for document layout analysis (document segmentation). It can be used to trained semantic segmentation/Object detection models.

NOTE

  • Models are trained on a portion of the dataset (train-0.zip, train-1.zip, train-2.zip, train-3.zip)
  • Trained on total 191,832 images
  • Models are evaluated on dev.zip (~11,000 images)
  • Backbone pretrained on COCO dataset is used but trained from scratch on PubLayNet dataset
  • Trained using Nvidia GTX 1080Ti 11GB
  • Trained on Windows 10

Steps to test pretrained models locally or jump to next section for docker deployment

from detectron2.data import MetadataCatalog
MetadataCatalog.get("dla_val").thing_classes = ['text', 'title', 'list', 'table', 'figure']