## Problem

The goal of this challenge is to develop a Vision-Language Model (VLM) capable of accurately understanding and relating the visual cues of food dishes to their corresponding textual descriptions. This task pushes the boundaries of **fine-grained visual recognition and multimodal alignment**, especially in a challenging and diverse domain as food. Recognizing the subtle visual features that differentiate similar dishes, ingredients, and preparation styles is crucial for a wide range of applications from dietary tracking to restaurant automation.

To support participants in building strong models, we provide a **training set consisting of 400,000 food image-caption pairs** obtained by the [Precision at Scale framework](https://arxiv.org/abs/2407.03463). The dataset is a combination of **synthetic and noisy real captions**. Note that some of the images may also be synthetic. The data will be provided in a format that includes each caption along with a **link to download the associated image**. We strongly encourage all participants to **download the images as early as possible** and to **report any issues** they may encounter during the download process, so we can address them quickly. As some training-set images belong to LAION, there is a possibility that some of them are being removed by the authors during the course of the challenge. 

To foster creativity, we allow participants to use **any external data sources** or pre-trained models (see "Limitations" and "Rules" for more information), as long as they **adhere to the license terms of this challenge and any third-party data** they incorporate. The focus is on achieving the **most accurate and detailed alignment between food images and text**, and we want to provide as much freedom as possible to explore different modeling strategies and data augmentation techniques.

The initial training data can be downloaded [from HuggingFace](https://huggingface.co/collections/jesusmolrdv/cvprw-mtf-2025-vlm-challenge-data-67e3b1cd766b4836df868a1b).