---
language:
- en
size_categories:
- 100K<n<1M
license: cc-by-nc-2.0
---


# MTF 2025 VLM Dishcovery Challenge Dataset - Web Split

## Overview

This dataset is part of the training data for the **CVPR Workshop Metafood 2025 (MTF 2025) Dishcovery VLM Challenge**. It consists of image-text pairs where the images are sourced from the web.

The dataset has been carefully curated using the **[Precision at Scale: Domain-Specific Datasets On-Demand](https://arxiv.org/abs/2407.03463)** method, ensuring high relevance and quality for domain-specific tasks.

**Associated Hugging Face Repository:** `jesusmolrdv/MTF25-VLM-Challenge-Dataset-Web`

## Dataset Description

* **Source:** Web-scraped images.
* **Format:** The dataset hosted on Hugging Face contains a table (similar to a Pandas DataFrame) with two columns:
    * `url`: The URL of the original food image.
    * `caption`: A textual description associated with the image.
* **Curation:** The selection of image-text pairs was performed using the methodology described in the "Precision at Scale" paper (see Citation section).

## How to Use / Download

Since this dataset split contains URLs to images rather than the images themselves, you need to download the images using a tool like [`img2dataset`](https://github.com/rom1504/img2dataset/).

1.  **Install dependencies:**
    ```bash
    pip install img2dataset datasets pyarrow pandas
    ```

2.  **Prepare the URL list:**
    First, download the dataset metadata (which contains the URLs and captions) from Hugging Face and save it as a local file (e.g., Parquet format). Run this short Python script:

    ```python
    from datasets import load_dataset
    import os
    
    hf_dataset_name = "jesusmolrdv/MTF25-VLM-Challenge-Dataset-Web"
    metadata_output_file = "mtf2025_web_metadata.parquet" # Output file for img2dataset
    
    print(f"Loading dataset metadata: {hf_dataset_name}")
    # Ensure you load the correct split, default is often 'train'
    dataset = load_dataset(hf_dataset_name, split="train")
    
    print(f"Saving metadata to {metadata_output_file}...")
    # Save in Parquet format, which img2dataset can read efficiently
    dataset.to_parquet(metadata_output_file)
    
    print("Metadata file saved successfully.")
    
    ```
    This script will create a file named `mtf2025_web_metadata.parquet` in the directory where you run it. This file contains the `url` and `caption` columns needed by `img2dataset`.

3.  **Download images using `img2dataset` CLI:**
    Now, use the `img2dataset` command in your terminal. Adjust parameters like `output_folder`, `image_size`, and `processes_count` as needed.

    ```bash
    img2dataset \
      --url_list mtf2025_web_metadata.parquet \
      --input_format "parquet" \
      --url_col "url" \
      --caption_col "caption" \
      --output_format webdataset \
      --output_folder ./mtf2025_web_images \
      --processes_count 16 \
      --thread_count 64 \
      --image_size 512 \
      --resize_mode keep_ratio \
      --enable_wandb False
    ```

> *Note: Downloading large datasets can take significant time and bandwidth. Some URLs might become inactive over time.*

## Citation

If you use this dataset in your research or challenge participation, please cite the following paper describing the curation method:

```bibtex
@misc{rodríguezdevera2024precisionscaledomainspecificdatasets,
      title={Precision at Scale: Domain-Specific Datasets On-Demand},
      author={Jesús M Rodríguez-de-Vera and Imanol G Estepa and Ignacio Sarasúa and Bhalaji Nagarajan and Petia Radeva},
      year={2024},
      eprint={2407.03463},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.03463},
}
```

## Acknowledgements

The author thankfully acknowledges the computer resources at Mare Nostrum 5 and the technical support provided by BSC (IM-2024-2-0022, IM-2023-3-0019).
We acknowledge EuroHPC Joint Undertaking for awarding us access to Mare Nostrum 5 at BSC, Spain.