File size: 1,227 Bytes
b67482d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | ---
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}
}
```
``` |