README.md

---
license: mit
task_categories:
- text-retrieval
- text-to-image
language:
- en
tags:
- cultural heritage
---
# REEVLAUATE Image-Text Pair Dataset

## Overview

This is an image-text pair dataset constructed for the **Knowledge-Enhanced Multimodal Retrieval System**, built upon the **REEVLAUATE KG ArtKB**. 
The dataset is designed for training and evaluating the CLIP model for the retrieval system.

## Data Source

The ArtKB knowledge base combines data from two primary sources:
- **Wikidata**
- **Pilot Museums**

## Dataset Structure

The dataset is organized into three splits:
- **Train**: Training set
- **Validation**: Validation set
- **Test**: Test set

Each split contains:
- **Images**: Visual content stored in subdirectories (`000/`, `001/`, ..., `999/`)
- **Texts**: Text descriptions paired with images, stored in corresponding subdirectories
- **metadata.parquet**: A Parquet file containing structured data for all samples in the split

## Data Format

### Directory Structure

```
hf_reevaluate_upload/
├── train/
│   ├── images/
│   │   ├── 000/
│   │   ├── 001/
│   │   └── ...
│   ├── texts/
│   │   ├── 000/
│   │   ├── 001/
│   │   └── ...
│   └── metadata.parquet
├── validation/
│   ├── images/
│   ├── texts/
│   └── metadata.parquet
└── test/
    ├── images/
    ├── texts/
    └── metadata.parquet
```

### Parquet Schema

Each sample in the Parquet files contains the following columns:

| Column | Type | Description |
|--------|------|-------------|
| `image` | string | Relative path to the image file |
| `uuid` | string | Unique identifier for the artwork |
| `query_text` | string | User query-like text |
| `target_text` | list[string] | Description text corresponding to the specific image including visual content and metadata information |
## Text Generation Methods

### 1. Metadata Portion
The **metadata** descriptions are constructed by combining multiple metadata fields from the ArtKB knowledge base using different templates. Each template produces a different textual representation of the same metadata information. This results in 5 distinct variants that capture the same facts in different phrasings.

**Example fields used:**
- Creator/Artist name
- Creation date
- Materials and techniques
- Dimensions
- Current location/Museum
- Object type and classification
- ...

### 2. Content Portion
The **content** descriptions are generated automatically using the **Salesforce/BLIP2-OPT-2.7B** vision-language model. These descriptions capture visual characteristics of the artwork observed directly from the image, such as composition, colors, subjects, and visual elements.

**Model**: `Salesforce/blip2-opt-2.7b`

### 3. Description Texts 
The **description text** descriptions are created by concatenating content portion with metadata protion:

```
[Content Portion] + [Metadata Portion]
```

## Usage

The dataset can be loaded and used with the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset('xuemduan/reevaluate-image-text-pairs')

# Access specific splits
train_set = load_dataset('xuemduan/reevaluate-image-text-pairs', split='train')
val_set = load_dataset('xuemduan/reevaluate-image-text-pairs', split='validation')
test_set = load_dataset('xuemduan/reevaluate-image-text-pairs', split='test')

# Iterate through samples
for sample in train_set:
    image_path = sample['image']
    uuid = sample['uuid']
    object_type = sample['object_type']  
    query_texts = sample['query_text']  
    description_text = sample['target_txt']  
```

## Citation

If you use this dataset in your research, please cite this dataset.

## Contact

For questions or issues related to this dataset, please email xuemin.duan@kuleuven.be