lifuguan/SAT
SAT: Spatial Aptitude Training for Multimodal Language Models Project Page To use the dataset, first make sure you have Python3.10 and Huggingface datasets version 3.0.2 (pip install datasets==3.0.2): from datasets import load_dataset import io split = "val" dataset = load_dataset("array/SAT", batch_size=128) example = dataset[split][10] # example 10th item images = [Image.open(io.BytesIO(im_bytes)) for im_bytes in example['image_bytes']] # this is a list of images. Some… See the full description on the dataset page: https://huggingface.co/datasets/lifuguan/SAT.
SAT: Spatial Aptitude Training for Multimodal Language Models
To use the dataset, first make sure you have Python3.10 and Huggingface datasets version 3.0.2 (pip install datasets==3.0.2):
from datasets import load_dataset
import io
split = "val"
dataset = load_dataset("array/SAT", batch_size=128)
example = dataset[split][10] # example 10th item
images = [Image.open(io.BytesIO(im_bytes)) for im_bytes in example['image_bytes']] # this is a list of images. Some questions are on one image, and some on 2 images
question = example['question']
answer_choices = example['answers']
correct_answer = example['correct_answer']
The available split choices are:
train: (175K image QA pairs) Train split of SAT data that includes both static relationships and dyamic spatial QAs involving object and scene motion. For motion-based questions, there are two images.static: (127K image QA pairs) Train split of SAT data that includes only static QAs. Always has one image only.val: (4K image QA pairs) Synthetic validation split.test: (150 image QA pairs) Real-image dynamic test set.
If you find this data useful, please consider citing:
@misc{ray2025satdynamicspatialaptitude,
title={SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models},
author={Arijit Ray and Jiafei Duan and Ellis Brown and Reuben Tan and Dina Bashkirova and Rose Hendrix and Kiana Ehsani and Aniruddha Kembhavi and Bryan A. Plummer and Ranjay Krishna and Kuo-Hao Zeng and Kate Saenko},
year={2025},
eprint={2412.07755},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.07755},
}