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ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding

Jovana Kondic, Pengyuan Li, Dhiraj Joshi, Isaac Sanchez, Ben Wiesel, Shafiq Abedin, Amit Alfassy, Eli Schwartz, Daniel Caraballo, Yagmur Gizem Cinar, Florian Scheidegger, Steven I. Ross, Daniel Karl I. Weidele, Hang Hua, Ekaterina Arutyunova, Roei Herzig, Zexue He, Zihan Wang, Xinyue Yu, Yunfei Zhao, Sicong Jiang, Minghao Liu, Qunshu Lin, Peter Staar, Luis Lastras, Aude Oliva, Rogerio Feris

Abstract

Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language models (VLMs) remain limited. We introduce ChartNet, a high-quality, million-scale multimodal dataset designed to advance chart interpretation and reasoning. ChartNet leverages a novel code-guided synthesis pipeline to generate 1.5 million diverse chart samples spanning 24 chart types and 6 plotting libraries. Each sample consists of five aligned components: plotting code, rendered chart image, data table, natural language summary, and question-answering with reasoning, providing fine-grained cross-modal alignment. To capture the full spectrum of chart comprehension, ChartNet additionally includes specialized subsets encompassing human annotated data, real-world data, safety, and grounding. Moreover, a rigorous quality-filtering pipeline ensures visual fidelity, semantic accuracy, and diversity across chart representations. Fine-tuning on ChartNet consistently improves results across benchmarks, demonstrating its utility as large-scale supervision for multimodal models. As the largest open-source dataset of its kind, ChartNet aims to support the development of foundation models with robust and generalizable capabilities for data visualization understanding. The dataset is publicly available at https://huggingface.co/datasets/ibm-granite/ChartNet

ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding

Abstract

Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language models (VLMs) remain limited. We introduce ChartNet, a high-quality, million-scale multimodal dataset designed to advance chart interpretation and reasoning. ChartNet leverages a novel code-guided synthesis pipeline to generate 1.5 million diverse chart samples spanning 24 chart types and 6 plotting libraries. Each sample consists of five aligned components: plotting code, rendered chart image, data table, natural language summary, and question-answering with reasoning, providing fine-grained cross-modal alignment. To capture the full spectrum of chart comprehension, ChartNet additionally includes specialized subsets encompassing human annotated data, real-world data, safety, and grounding. Moreover, a rigorous quality-filtering pipeline ensures visual fidelity, semantic accuracy, and diversity across chart representations. Fine-tuning on ChartNet consistently improves results across benchmarks, demonstrating its utility as large-scale supervision for multimodal models. As the largest open-source dataset of its kind, ChartNet aims to support the development of foundation models with robust and generalizable capabilities for data visualization understanding. The dataset is publicly available at https://huggingface.co/datasets/ibm-granite/ChartNet

Paper Structure

This paper contains 57 sections, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Large Multimodal Models.
  4. Chart Understanding Datasets
  5. Synthetic Data Generation for Vision-Language Models
  6. ChartNet Data Generation Pipeline
  7. Code-Guided Data Generation At Scale
  8. QA Pairs with CoT Reasoning
  9. Models and Compute Infrastructure
  10. Quality Filtering Evaluation
  11. The ChartNet Dataset
  12. Core Dataset
  13. Human-Annotated Synthetic Chart Data
  14. High-Quality Real-World Charts
  15. Grounding QA Pairs
  16. ...and 42 more sections

Figures (14)

  • Figure 1: Code-guided chart augmentation: First, a seed chart image is passed to a vision-language model for chart reconstruction -- translating the image into executable plotting code. Then, the generated code is passed to a large language model, and iteratively augmented to collect diverse outputs.
  • Figure 2: An illustration of synthetic chart images generated from a single seed chart using the ChartNet pipeline. A seed chart is first translated into approximate plotting code, which is executed to render a reconstructed chart. The code is then iteratively augmented to produce diverse variations in chart types, styles, and representations, as shown in the subsequent augmentations.
  • Figure 3: Data attributes, chart types, and plotting packages included in ChartNet.
  • Figure 4: Distribution of chart types generated for ChartNet.
  • Figure 5: Distribution of plotting packages used in ChartNet.
  • ...and 9 more figures