ImageNet-Think-250K: A Large-Scale Synthetic Dataset for Multimodal Reasoning for Vision Language Models
This provides a resource for training and evaluating multimodal reasoning models, potentially aiding research in robust VLMs and understanding reasoning mechanisms, but it is incremental as it builds on existing datasets and models.
The authors tackled the problem of developing Vision Language Models (VLMs) with explicit reasoning capabilities by creating ImageNet-Think-250K, a large-scale synthetic dataset of 250,000 images with structured thinking tokens and answers, generated using state-of-the-art VLMs to capture step-by-step reasoning processes.
We develop ImageNet-Think, a multimodal reasoning dataset designed to aid the development of Vision Language Models (VLMs) with explicit reasoning capabilities. Our dataset is built on 250,000 images from ImageNet21k dataset, providing structured thinking tokens and corresponding answers. Our synthetic dataset is generated by two state-of-the-art VLMs: GLM-4.1V-9B-Thinking and Kimi-VL-A3B-Thinking-2506. Each image is accompanied by two pairs of thinking-answer sequences, creating a resource for training and evaluating multimodal reasoning models. We capture the step-by-step reasoning process of VLMs and the final descriptive answers. Our goal with this dataset is to enable the development of more robust VLMs while contributing to the broader understanding of multimodal reasoning mechanisms. The dataset and evaluation benchmarks will be publicly available to aid research in reasoning/thinking multimodal VLMs.