ChartNet: A Million-Scale Dataset for Multimodal Chart Interpretation

ChartNet is a large-scale, high-quality multimodal dataset designed for robust chart understanding and reasoning. It contains over one million chart samples, combining geometric visual patterns, structured numerical data, and natural language descriptions. The dataset was created by IBM Granite and was last updated in March 2026.

Use Cases

• Train models for visual question answering using chart images and associated textual questions.
• Generate natural language descriptions or summaries from chart images and their underlying structured data.
• Perform table question answering by extracting and reasoning over the numerical data embedded within charts.
• Benchmark model performance on multimodal tasks involving image-to-text conversion for data visualization.

Strengths

• Contains over one million chart samples, providing a substantial scale for training.
• Designed as a high-quality, multimodal resource specifically for chart interpretation.

Limitations

• Specific column names, data formats, and exact row counts are not detailed in the provided description.
• The dataset's license, geographic scope, and precise temporal coverage are unknown.

Provenance

Source

IBM Granite (ibm-granite)

Freshness

Last updated on 2026-03-31.