README.md

metadata

tags:
  - image-retrieval
  - benchmark
  - photobench
  - vision-language
license: cc-by-nc-4.0
size_categories:
  - n<1K
task_categories:
  - text-to-image
language:
  - en
  - zh

Quick Links

🏠 GitHub · 📄 arXiv · 🏅 Leaderboard · 📦 Dataset · 🏅 Protected Leaderboard · 📦 Protected Dataset · 🖼️ Raw Images: obox · Google Drive (pwd: Oppo2026)

---

PhotoBench

PhotoBench is the first benchmark constructed from authentic, personal albums, designed to shift the paradigm from visual matching to personalized multi-source intent-driven photo retrieval.

Leaderboard: PhotoBench Leaderboard

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Dataset Description

PhotoBench is an image retrieval benchmark with open-ended natural language queries. Unlike the protected version, PhotoBench gives you unrestricted access to the raw images, allowing you to use your own embedding models, caption generators, or agent-based retrieval workflows.

This dataset contains:

• Test queries for leaderboard submission (English + Chinese)
• Validation queries with released ground truth for local self-evaluation
• Raw images for all 3 albums (available upon request; not included in this repository due to size)

Album	Images	Test Queries	Validation Queries
1	~1,070	382	100
2	~1,470	236	100
3	~1,050	269	100
Total		887	300

Note:

• Use albumN_test.json if you want to submit to the PhotoBench Leaderboard. Ground truth is hidden and evaluated on the server.
• Use albumN_validation.json if you want to evaluate your model locally. Ground truth is included in this file.

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Two Variants

PhotoBench is released in two variants to support different research directions:

	PhotoBench (Full)	PhotoBench-Protected
Images	Raw original photos (~11 GB)	Not included
Features	Use your own models (CLIP, SigLIP, etc.)	Pre-computed captions & embeddings provided
Metadata	Extract your own (EXIF, timestamps, etc.)	Pre-computed metadata provided
Focus	Unrestricted retrieval: embedding, caption, or agent	Agent planning only
Leaderboard	PhotoBench	PhotoBench-Protected
Dataset	SorrowTea/PhotoBench	SorrowTea/PhotoBench-Protected

• PhotoBench (Full) — For researchers who want to experiment with their own vision encoders, caption generators, or end-to-end agent pipelines. You get the raw images and complete freedom.
• PhotoBench-Protected — For researchers focusing exclusively on agent planning and reasoning. No raw images are provided; you must work with pre-computed captions, embeddings, and metadata. This isolates the planning component from visual representation learning.

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Data Format

Test Queries (albumN_test.json)

For leaderboard submission. Each file is a JSON array of query objects:

[
  {
    "query_cn": "摆满的书桌",
    "query_en": "cluttered desk"
  }
]

Field	Type	Description
query_cn	string	Query text in Chinese
query_en	string	Query text in English (primary language)

Validation Queries (albumN_validation.json)

For local self-evaluation. Each file is a JSON array of query objects with released ground truth:

[
  {
    "query_cn": "烧香的三姐妹",
    "query_en": "three sisters offering incense",
    "ground_truth": ["IMG_4906.JPG"]
  }
]

Field	Type	Description
query_cn	string	Query text in Chinese
query_en	string	Query text in English (primary language)
ground_truth	string[]	List of correct image filenames for this query

[
  {
    "query_cn": "摆满的书桌",
    "query_en": "cluttered desk"
  },
  {
    "query_cn": "紫毛衣女孩",
    "query_en": "girl in purple sweater"
  }
]

Field	Type	Description
query_cn	string	Query text in Chinese
query_en	string	Query text in English (primary language)

Raw Images

The raw images (album1/, album2/, album3/) contain the full-resolution original photos.

Total size: ~11 GB
Format: JPEG
Naming: Original camera filenames (e.g., IMG_1234.JPG, FullSizeRender.JPG)

Raw images are not hosted in this repository due to size constraints. Please contact the authors or use the download instructions below.

---

How to Use

1. Download Queries

Both test and validation JSON files are available directly in this repository:

# Via huggingface_hub CLI
huggingface-cli download SorrowTea/PhotoBench-Full-HF --repo-type dataset --local-dir ./photobench

Or browse and download individual files from the Files tab above.

• test/albumN_test.json — for leaderboard submission
• validation/albumN_validation.json — for local self-evaluation

2. Download Raw Images

Raw images are distributed separately. Contact the authors for access, or prepare the images according to the album structure:

raw_albums/
├── album1/
│   ├── IMG_0001.JPG
│   ├── IMG_0002.JPG
│   └── ...
├── album2/
└── album3/

3. Build Your Retrieval System

With the raw images and test queries, you can:

• Extract image embeddings with any vision encoder (CLIP, SigLIP, etc.)
• Generate captions with any VLM (GPT-4V, Qwen-VL, etc.)
• Design multi-step agent workflows
• Evaluate with your own metrics

4. Build the Submission File

The dataset provides one albumN_test.json per album. Before submitting, you must combine all albums into a single JSON array and add the album_id field to each query object:

Step-by-step:

1. Load album1_test.json, album2_test.json, and album3_test.json.
2. For each query object, add "album_id": "1" (or "2" / "3").
3. Add a "pred" field containing the ordered list of predicted image filenames.
4. Merge all queries into one JSON array and save as submission.json.

Example transformation:

import json

submission = []
for album_id in ["1", "2", "3"]:
    with open(f"album{album_id}_test.json") as f:
        queries = json.load(f)
    for q in queries:
        submission.append({
            "album_id": album_id,
            "query_en": q["query_en"],
            "pred": ["IMG_0001.JPG", "IMG_0002.JPG", ...]  # your predictions
        })

with open("submission.json", "w") as f:
    json.dump(submission, f, indent=2)

Submission format:

[
  {
    "album_id": "1",
    "query_en": "cluttered desk",
    "pred": ["IMG_1234.JPG", "IMG_5678.JPG", "IMG_9012.JPG"]
  }
]

Requirements:

• album_id: "1", "2", or "3" (string).
• query_en: Must match the test query exactly (case-sensitive).
• pred: Ordered list of predicted image filenames. Order matters for NDCG.

5. Submit to Leaderboard

Upload submission.json to the PhotoBench Leaderboard.

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Evaluation

The leaderboard computes the following metrics:

Metric	Description
Recall@k	Proportion of ground-truth images in top-k
NDCG@k	Normalized Discounted Cumulative Gain at rank k

Supported k values: 1, 5, 10, 20, 50, 100

Results are averaged per album, then averaged across albums for the final score.

Only full submissions (all 3 albums, all queries) are eligible for public leaderboard ranking.

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Citation

If you use PhotoBench in your research, please cite:

@misc{photobench2026,
  title={PhotoBench},
  year={2026},
  eprint={2603.01493},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

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License

This dataset is released under the MIT License.

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Contact

For questions or data access requests, please open an issue on this repository or contact the authors.