---
dataset_info:
  features:
  - name: pair_id
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: image_1
    dtype: string
  - name: image_2
    dtype: string
  - name: idx
    dtype: string
  - name: supercategory
    dtype: string
  - name: category
    dtype: string
  - name: type
    dtype: string
  - name: source_json
    dtype: string
  splits:
  - name: train
    num_bytes: 365770574
    num_examples: 561569
  download_size: 14528564
  dataset_size: 365770574
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
task_categories:
- visual-question-answering
language:
- en
tags:
- finegrained
- finegrained-vqa
pretty_name: TWIN
size_categories:
- 100K<n<1M
---

# TWIN 
This repository contains the TWIN dataset introduced in the paper [Same or Not? Enhancing Visual Perception in Vision-Language Models](https://glab-caltech.github.io/twin). TWIN contains 561K challenging (image, question, answer) tuples emphasizing fine-grained image understanding.

For evaluating on the dataset with LMMS-eval, please refer to this [repo](https://github.com/damianomarsili/lmms-eval).

## Citation 
If you use the TWIN dataset in your research, please use the following BibTeX entry.
```
@misc{marsili2025notenhancingvisualperception,
      title={Same or Not? Enhancing Visual Perception in Vision-Language Models}, 
      author={Damiano Marsili and Aditya Mehta and Ryan Y. Lin and Georgia Gkioxari},
      year={2025},
      eprint={2512.23592},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2512.23592}, 
}
```