https://github.com/paparazz1/auto-timeline-v2
https://github.com/paparazz1/auto-timeline-v2
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/paparazz1/auto-timeline-v2
- Owner: PaParaZz1
- License: mit
- Created: 2019-10-25T12:56:03.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-11-07T10:23:49.000Z (over 5 years ago)
- Last Synced: 2025-01-05T21:12:15.334Z (5 months ago)
- Language: Python
- Size: 5.09 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Learning-based Auto-Extraction of Extensible Timeline
## Introduction
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](https://github.com/facebookresearch/maskrcnn-benchmark).
You can also check our [project webpage](https://chenzhutian.org/auto-infog-timeline/) for more details.
In this repository, we release code and data for training the deep learning model
in our method.
## Citation
If you find our work useful in your research, please consider citing:
```
@ARTICLE{chen19,
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}
}
```
## Installation
Check [INSTALL.md](INSTALL.md) for installation instructions.
## Dataset
The real world timeline dataset we used can be downlowded [here](https://hkustconnect-my.sharepoint.com/:u:/g/personal/zchenbn_connect_ust_hk/ERa04v7ULwlJpvn_0YRRtHsBF7QFBp77t_7caxb8QR2QVg?e=ZKfomX).
The dataset contains images of timeline infographics with coco format annotations,
divided into `train` and `val`:
```
timeline.zip
|
| - 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/paths_catalog.py`.
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](https://hkustconnect-my.sharepoint.com/:u:/g/personal/zchenbn_connect_ust_hk/ESmSvxTW2cFJuV1vc5Z7H9UB9tgb1rQtttYYVSWj1b0kcg?e=MVFt3g).
## Training
### Multi-GPU training
Run the following command in the root of this repo:
```bash
export NGPUS=2
python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_benchmark/tools/train_net.py --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**
```bash
python /path_to_maskrcnn_benchmark/tools/train_net.py --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).
## Testing
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.
## Troubleshooting
If you have issues running or compiling this code, we have compiled a list of common issues in
[TROUBLESHOOTING.md](TROUBLESHOOTING.md). If your issue is not present there, please feel
free to open a new issue.
## Note
The code in this repo is under active development.
## License
Our code is released under MIT License (see LICENSE file for details).