Detectron2 for Document Layout Analysis

Detectron2 trained on PubLayNet dataset

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

• Install the latest Detectron2 from https://github.com/facebookresearch/detectron2
• Copy config files (DLA_*) from this repo to the installed Detectron2
• Download the relevant model from the Benchmarking section. If you have downloaded model using wget then refer https://github.com/hpanwar08/detectron2/issues/22
• Add the below code in demo/demo.py in the mainto get confidence along with label names

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

• Then run below command for prediction on single image (change the config file relevant to the model)

python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input "<path to image.jpg>" --output <path to save the predicted image> --confidence-threshold 0.5 --opts MODEL.WEIGHTS <path to model_final_trimmed.pth> MODEL.DEVICE cpu

Docker Deployment

• For local docker deployment for testing use Docker DLA

Benchmarking

Configuration used for training

Some helper code and cli commands

Add the below code in demo/demo.py to get confidence along with label names

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

Then run below command for prediction on single image

python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input "<path to image.jpg>" --output <path to save the predicted image> --confidence-threshold 0.5 --opts MODEL.WEIGHTS <path to model_final_trimmed.pth> MODEL.DEVICE cpu

TODOs ⏰

• Train MaskRCNN resnet50

Sample results from detectron2

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark.

What's New

• It is powered by the PyTorch deep learning framework.
• Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
• Can be used as a library to support different projects on top of it. We'll open source more research projects in this way.
• It trains much faster.

See our blog post to see more demos and learn about detectron2.

Installation

See INSTALL.md.

Quick Start

See GETTING_STARTED.md, or the Colab Notebook.

Learn more at our documentation. And see projects/ for some projects that are built on top of detectron2.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the Detectron2 Model Zoo.

License

Detectron2 is released under the Apache 2.0 license.

Citing Detectron

If you use Detectron2 in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.

@misc{wu2019detectron2,
  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
                  Wan-Yen Lo and Ross Girshick},
  title =        {Detectron2},
  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
  year =         {2019}
}