| --- |
| license: apache-2.0 |
| tags: |
| - object-detection |
| - chart-understanding |
| - document-ai |
| - cascade-rcnn |
| - swin-transformer |
| --- |
| |
| # Chart Element Detector |
|
|
| A chart element detection model based on |
| [CACHED](https://github.com/pengyu965/ChartDete) |
| (Context-Aware Chart Element Detection). |
|
|
| ## Model Details |
|
|
| - **Architecture**: Cascade R-CNN + Swin Transformer + FPN |
| - **Task**: Chart element detection and localization |
| - **Classes**: 18 chart element classes |
| - **Dataset**: PMC Chart Dataset |
| - **COCO AP**: 0.729 |
|
|
| ## Classes |
|
|
| x_tick_label, y_tick_label, x_tick, y_tick, |
| x_axis_title, y_axis_title, chart_title, |
| legend_marker, legend_label, legend_title, |
| value_label, mark_label, tick_grouping, |
| plot_area, x_axis_area, y_axis_area, |
| legend_area, others |
| |
| ## Output Format |
| ```json |
| { |
| "chart": [ |
| { |
| "x1": 10.0, |
| "y1": 20.0, |
| "x2": 100.0, |
| "y2": 200.0, |
| "score": 0.95, |
| "class": "chart_title" |
| } |
| ] |
| } |
| ``` |
| |
| ## Requirements |
| ``` |
| torch==1.13.1 |
| mmdet==2.28.2 |
| mmcv-full==1.7.0 |
| ``` |
|
|
| ## Citation |
| ```bibtex |
| @inproceedings{yan2023cached, |
| title={CACHED: Context-Aware Chart Element Detection}, |
| author={Yan, Pengyu and Ahmed, Saleem and Doermann, David}, |
| booktitle={ICDAR}, |
| year={2023} |
| } |
| ``` |
| ``` |
