SpaceThinker: Synthetic Spatial Reasoning Traces for Vision-Language Models

A dataset created by remyxai and last updated on April 23, 2025. It is designed for training LLaVA-style Vision-Language Models and contains synthesized spatial reasoning traces. The data was generated using VQASynth from a subset of images in the localized narratives split of the cauldron.

Use Cases

• Training Vision-Language Models for spatial question-answering based on the described spatial reasoning traces.
• Benchmarking model performance on quantitative distance estimation tasks based on prompts like 'How far is the man in the red hat from the pallet of boxes in feet?'
• Synthesizing and studying reasoning processes for visual tasks based on the VQASynth generation method mentioned.

Strengths

• Dataset is specifically designed for a concrete task: training LLaVA-style Vision-Language Models.
• Data generation method is explicitly stated: created using VQASynth.
• Last update timestamp is recent and precise: 2025-04-23 20:09:55.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface, author remyxai

Collection Method

Synthesized using VQASynth from images in the localized narratives split of the cauldron.

Time Range

null

Freshness

Last updated 2025-04-23 20:09:55.

Geography

null