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Deep Learning-based Auto-Extraction of Extensible Timeline


This project, which is going to appear in IEEE VIS 2019, aims at extracting an extensible template from a bitmap timeline infogrpahic using a deep learning model. This project is builted based on maskrcnn-benchmark. You can also check our project webpage for more details.

In this repository, we release code and data for training the deep learning model in our method.


If you find our work useful in your research, please consider citing:

  author  = {Zhutian Chen and Yun Wang and Qianwen Wang and Yong Wang and Huamin Qu},
  title   = {{Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline}},
  journal = {{IEEE Transactions on Visualization and Computer Graphics}},
  year    = {2018}, 
  volume  = {}, 
  number  = {}, 
  pages   = {1-1}


Check for installation instructions.


The real world timeline dataset we used can be downlowded here. The dataset contains images of timeline infographics with coco format annotations, divided into train and val:
  | - train
  |     | - coco_format.json // the annotations of the training images
  |     | - f002.jpg
  |     | - f002.T.jpg // the rotated f001.jpg
  |     | ...
  | - val
  |    | - coco_format.json // the annotations of the testing images
  |    | - f001.jpg
  |    | ...

Create a folder dataset/coco in the root of this reprositary and then copy and unzip the dataset under the dataset/coco folder. The details of path configurations can be found in maskrcn_benchmark/config/

We also provide the single annotation for each image for those who want to re-split the images into train and test. The annotations can be downloaded here.


Multi-GPU training

Run the following command in the root of this repo:

export NGPUS=2
python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_benchmark/tools/ --config-file "path/to/config/file.yaml"

Single GPU training

The defualt configuration files that we provide are used for running on 2 GPUs. In order to run it on a single GPU, there are a few possibilities:

1. Run the following without modifications

python /path_to_maskrcnn_benchmark/tools/ --config-file "/path/to/config/file.yaml"

This should work out of the box and is very similar to what we should do for multi-GPU training. But the drawback is that it will use much more GPU memory. The reason is that we set in the configuration files a global batch size that is divided over the number of GPUs. So if we only have a single GPU, this means that the batch size for that GPU will be 8x larger, which might lead to out-of-memory errors.

If you have a lot of memory available, this is the easiest solution.

2. Modify the cfg parameters

If you experience out-of-memory errors, you can reduce the global batch size. But this means that you'll also need to change the learning rate (to half), and the number of iterations (to double).


After the training, the programm will conduct an evaluation and output the evaluation results. If you re-run the training without changing the OUTPUT_DIR, the programm will load the last result in OUTPUT_DIR and directly run the evaluation code by skiping the training.


If you have issues running or compiling this code, we have compiled a list of common issues in If your issue is not present there, please feel free to open a new issue.


The code in this repo is under active development.


Our code is released under MIT License (see LICENSE file for details).

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