README.md

---
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

<p align="center">
  🏠 <a href="https://github.com/LaVieEnRose365/PhotoBench"><strong>GitHub</strong></a> ·
  📄 <a href="https://arxiv.org/abs/2603.01493v1"><strong>arXiv</strong></a> ·
  🏅 <a href="https://huggingface.co/spaces/SorrowTea/PhotoBench/"><strong>Leaderboard</strong></a> ·
  📦 <strong>Dataset</strong> ·
  🏅 <a href="https://huggingface.co/spaces/SorrowTea/PhotoBench-Protected/"><strong>Protected Leaderboard</strong></a> ·
  📦 <a href="https://huggingface.co/datasets/SorrowTea/PhotoBench-Protected"><strong>Protected Dataset</strong></a> ·
  🖼️ Raw Images: <a href="https://sbox.myoas.com/l/Be5be4053f6b43840"><strong>obox</strong></a> · <a href="https://drive.google.com/drive/folders/1IyAlRskgnXG6pJ7fYL5Ie0SFDK95-cI?usp=drive_link"><strong>Google Drive</strong></a> (pwd: Oppo2026)
</p>

---

# 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](https://huggingface.co/spaces/SorrowTea/PhotoBench/)

---

## 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](https://huggingface.co/spaces/SorrowTea/PhotoBench/). 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.

---

## 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](https://huggingface.co/spaces/SorrowTea/PhotoBench/) | [PhotoBench-Protected](https://huggingface.co/spaces/SorrowTea/PhotoBench-Protected/) |
| **Dataset** | [SorrowTea/PhotoBench](https://huggingface.co/datasets/SorrowTea/PhotoBench) | [SorrowTea/PhotoBench-Protected](https://huggingface.co/datasets/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.

---

## Data Format

### Test Queries (`albumN_test.json`)

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

```json
[
  {
    "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:

```json
[
  {
    "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 |

```json
[
  {
    "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:

```bash
# 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:**

```python
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:**

```json
[
  {
    "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](https://huggingface.co/spaces/SorrowTea/PhotoBench/).

---

## 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.

---

## Citation

If you use PhotoBench in your research, please cite:

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

---

## License

This dataset is released under the MIT License.

---

## Contact

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