README.md

metadata

task_categories:
  - visual-question-answering
language:
  - en
tags:
  - image
  - text
  - vlm
  - spatial-perception
  - spatial-reasoning
annotations_creators:
  - expert-generated
pretty_name: SPHERE
size_categories:
  - 1K<n<10K
source_datasets:
  - MS COCO-2017
configs:
  - config_name: distance_and_counting
    data_files: combine_2_skill/distance_and_counting.parquet
  - config_name: distance_and_size
    data_files: combine_2_skill/distance_and_size.parquet
  - config_name: position_and_counting
    data_files: combine_2_skill/position_and_counting.parquet
  - config_name: object_manipulation
    data_files: reasoning/object_manipulation.parquet
  - config_name: object_manipulation_w_intermediate
    data_files: reasoning/object_manipulation_w_intermediate.parquet
  - config_name: object_occlusion
    data_files: reasoning/object_occlusion.parquet
  - config_name: object_occlusion_w_intermediate
    data_files: reasoning/object_occlusion_w_intermediate.parquet
  - config_name: counting_only-paired-distance_and_counting
    data_files: single_skill/counting_only-paired-distance_and_counting.parquet
  - config_name: counting_only-paired-position_and_counting
    data_files: single_skill/counting_only-paired-position_and_counting.parquet
  - config_name: distance_only
    data_files: single_skill/distance_only.parquet
  - config_name: position_only
    data_files: single_skill/position_only.parquet
  - config_name: distance_only
    data_files: single_skill/size_only.parquet

SPHERE (Spatial Perception and Hierarchical Evaluation of REasoning) is a benchmark for assessing spatial reasoning in vision-language models. It introduces a hierarchical evaluation framework with a human-annotated dataset, testing models on tasks ranging from basic spatial understanding to complex multi-skill reasoning. SPHERE poses significant challenges for both state-of-the-art open-source and proprietary models, revealing critical gaps in spatial cognition.

Dataset Usage

This version of the dataset is prepared by combining the JSON annotations with the corresponding images from MS COCO-2017. The script used can be found at prepare_parquet.py, to be executed in the root of our GitHub repository.

Please note that the images are subject to the Terms of Use of MS COCO-2017:

Images

The COCO Consortium does not own the copyright of the images. Use of the images must abide by the Flickr Terms of Use. The users of the images accept full responsibility for the use of the dataset, including but not limited to the use of any copies of copyrighted images that they may create from the dataset.