Dataset Viewer
Auto-converted to Parquet Duplicate
info
dict
licenses
list
images
list
annotations
list
categories
list
{"description":"COCO 2014 Dataset","url":"http://cocodataset.org","version":"1.0","year":2014,"contr(...TRUNCATED)
[{"url":"http://creativecommons.org/licenses/by-nc-sa/2.0/","id":1,"name":"Attribution-NonCommercial(...TRUNCATED)
[{"license":3,"file_name":"COCO_val2014_000000166532.jpg","coco_url":"http://images.cocodataset.org/(...TRUNCATED)
[{"segmentation":[[306.34,532.13,300.58,514.88,293.39,503.37,299.15,496.18,299.15,493.3,299.15,483.2(...TRUNCATED)
[{"supercategory":"person","id":1,"name":"person"},{"supercategory":"vehicle","id":2,"name":"bicycle(...TRUNCATED)

AIMS Benchmarks

This repository contains the benchmark data and evaluation resources used in AIMS (Adaptive Information Multi-source Steering).

Contents

AIMS_Benchmarks/
├── MME_Benchmark_release_version/   # MME
├── amber/                           # AMBER
├── chair_coco/                      # CHAIR
├── faithscore/                      # FaithScore
└── nltk_3-8-1/                      # NLTK resources

The nltk_3-8-1 directory contains the taggers, tokenizers, and corpora required by the evaluation scripts. These resources correspond to NLTK 3.8.1 and are provided for convenient offline evaluation.

Usage

  • Download the whole repository:
hf download VisionXLab/AIMS_Benchmarks --local-dir <your_local_path> --repo-type dataset
  • Download a specific directory (e.g., AMBER):
hf download VisionXLab/AIMS_Benchmarks --local-dir <your_local_path> --include "amber/**" --repo-type dataset

Acknowledgements

The included benchmarks and resources are collected from their original projects for reproducible evaluation. Please refer to and cite the original works of MME, AMBER, CHAIR, FaithScore, MSCOCO, and NLTK when using the corresponding resources.

Cite Us

@article{aims,
  title   = {AIMS: Adaptive Information Multi-source Steering for Hallucination Mitigation in Large Vision-Language Models},
  author  = {...},
  journal = {arXiv preprint},
  year    = {2026}
}
Downloads last month
116