README.md

metadata

language:
  - en
pretty_name: SnapBench
task_categories:
  - visual-document-retrieval
tags:
  - multimodal
  - image-text-retrieval
  - retrieval
  - robustness
  - benchmark
configs:
  - config_name: queries
    data_dir: queries
    default: true
  - config_name: gallery
    data_dir: gallery

Source repository: https://github.com/zrchen03/SnapBench

Paper: SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions

SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions

SnapBench is a benchmark for snap-and-ask mobile interactions: a user captures a photo and asks a short English question. Each query pairs an image with text; the gallery contains image–caption pairs to retrieve from. The benchmark includes a clean split plus text and image perturbations to simulate real-world query degradation.

Queries	1,145 (image + text)
Gallery	9,085 items (image + caption)
Conditions	54 (1 clean + 8 text + 45 image perturbations)

This repository ships the clean benchmark in the standard Hugging Face ImageFolder format. It includes the clean query and gallery images, retrieval annotations, and perturbation metadata. It does not include evaluation code.

---

What Is Included

Component	Location	Status
Query metadata	queries/test/metadata.jsonl	included
Query images	queries/test/images/ (1,145)	included
Gallery metadata	gallery/test/metadata.jsonl (9,085 items)	included
Gallery images	gallery/test/images/ (9,059 unique images)	included
Text perturbations	queries/test/metadata.jsonl → text_perturbations	included
Image perturbation specifications	queries/test/metadata.jsonl → image_perturbations	included
Image perturbation files	15 types × 3 severity levels × 1,145 queries	generate locally from the source repository

---

Setup

Install Hugging Face Datasets:

pip install datasets

Load the two dataset configurations from the Hub:

from datasets import load_dataset

queries = load_dataset("yefd/SnapBench", "queries", split="test")
gallery = load_dataset("yefd/SnapBench", "gallery", split="test")

To load a local copy of this repository:

from datasets import load_dataset

queries = load_dataset(".", "queries", split="test")
gallery = load_dataset(".", "gallery", split="test")

The file_name field in each metadata.jsonl file is automatically exposed as an image column by the Hugging Face ImageFolder builder.

---

Build the Full Benchmark

This Hugging Face version already includes the clean benchmark (queries, gallery, and text perturbations). The image perturbation specifications are stored in the image_perturbations field of every query. Use the scripts in the source repository to generate all image-perturbed query images locally.

Step 1. Generate image perturbations

Clone and set up the source repository:

git lfs install
git clone https://github.com/zrchen03/SnapBench.git SnapBench_raw
cd SnapBench_raw
git lfs pull
pip install -r requirements.txt
export BENCH_IMAGES_DIR=$(pwd)/bench_images

Generate 15 perturbation types × 3 severity levels (sev1 / sev2 / sev3) × 1,145 queries = 51,675 images:

python benchmark/gen_image_perturbations.py

Output:

bench_images/perturbed/{type}/sev{1,2,3}/{query_id}.jpg

Preview the workload without writing files:

python benchmark/gen_image_perturbations.py --dry-run

To export the generated images as an additional Hugging Face configuration:

python benchmark/export_hf_dataset.py --include-perturbed --overwrite

Step 2. (Optional) Regenerate text perturbations

Text perturbations are already stored in queries/test/metadata.jsonl. Only rerun this in the source repository if you need to rebuild them:

python benchmark/gen_text_perturbations.py \
  --gpu 0 --chunk-in chunk_0.json --chunk-out result_0.json

---

Data Layout

SnapBench/
├── README.md
├── queries/
│   └── test/
│       ├── metadata.jsonl
│       └── images/          # 1,145 query images
└── gallery/
    └── test/
        ├── metadata.jsonl
        └── images/          # 9,059 unique gallery images

The dataset exposes two configurations, both with a test split:

• queries: clean query images and text, positive and hard-negative gallery IDs, and all text/image perturbation metadata.
• gallery: image–caption retrieval candidates. It has 9,085 items backed by 9,059 unique image files because some images have more than one caption.

Important fields:

• Query image: queries/test/metadata.jsonl → file_name (loaded as image)
• Query text: text
• Positive gallery items: positive_gallery_ids
• Hard negatives: hard_negative_gallery_ids
• Gallery image: gallery/test/metadata.jsonl → file_name (loaded as image)
• Gallery caption: caption