jesusmolrdv/MTF25-VLM-Challenge-Dataset-Web

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 method, ensuring high relevance and quality for domain-specific tasks.

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

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.

1. Install dependencies:

   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:

   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.

   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:

@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},
}