Poster

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 ⋅ Zihan Wang ⋅ Xinyue Yu ⋅ Yunfei Zhao ⋅ Sicong Jiang ⋅ Minghao Liu ⋅ Qunshu Lin ⋅ 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. A rigorous quality-filtering pipeline ensures visual fidelity, semantic accuracy, and diversity across chart representations. Fine-tuning on ChartNet consistently improves results across our 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. We will make both ChartNet data and models publicly available.