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End of preview. Expand in Data Studio
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
ECharts-SynChart: A Synthetic Dataset for Chart Classification
This repository contains the code and dataset for the paper "ECharts-SynChart: A Large-Scale Synthetic Dataset for Chart Type Classification".
Dataset
- Size: 10,272 images, 24 chart types.
- Source: Generated from ECharts official examples with data augmentation (numerical perturbation, color variation, label replacement).
- Download: https://doi.org/10.5281/zenodo.19401089
The dataset is accompanied by train/val/test CSV split files (included in this repository). The dataset is provided as a single compressed archive (echarts-synchart-images.tar.gz, ~486 MB). After downloading, extract the images; the archive contains an images/folder. Place this images/folder in the repository root (alongside README.md).
Code Structure
scripts/– Python scripts for data augmentation, training, and evaluation.train_classifier.py– Train ResNet50 baseline model.plot_confusion_matrix.py– Generate confusion matrix on test set.csv_to_jsonl.py– Convert CSV labels to JSONL format (for Qwen-VL).unified_augment.py– Data augmentation for ECharts JS files.generate_labels.py– Extract chart type labels from JS files.merge_datasets.py– Merge multiple PNG directories into one dataset.check_png.py– Detect and optionally delete blank images.config.py&utils.py– Configuration and helper functions.
node_scripts/– Node.js scripts for rendering ECharts JS to PNG.render_echarts.js– Render a JS file to PNG using Puppeteer.
requirements.txt– Python dependencies.
Quick Start
- Clone this repository
git clone https://github.com/dfps-1234/echarts-synchart.git
cd echarts-synchart
- Install dependencies
pip install -r requirements.txt
- Download the dataset
Download the archive from Zenodo and extract it:tar -xzf echarts-synchart-images.tar.gz # 解压后得到 images/ 文件夹 - Train the baseline model
# Ensure DATA_ROOT in train_classifier.py points to the directory containing images/ (e.g., DATA_ROOT = '.') python scripts/train_classifier.py --batch_size 64 --epochs 20 - Generate confusion matrix
# The confusion matrix script uses the same DATA_ROOT setting as train_classifier.py. python scripts/plot_confusion_matrix.py
Requirements
Python 3.8 or higher is required. See requirements.txt for a full list. Main packages:
- torch >= 2.0.0
- torchvision >= 0.15.0
- scikit-learn >= 1.2.0
- matplotlib >= 3.5.0
- pandas >= 1.5.0
- tqdm >= 4.65.0
- Pillow >= 9.0.0
- numpy >= 1.23.0
Citation
If you use this code or dataset in your research, please cite:
@article{li2024echarts,
title={ECharts-SynChart: A Large-Scale Synthetic Dataset for Chart Type Classification},
author={Li, Yunzhe},
journal={...},
year={2024},
doi={10.5281/zenodo.19401089}
}
License
This project is licensed under the MIT License – see the LICENSE file for details.
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