---
title: flickr30k
canonical_url: "https://www.modelscope.cn/datasets/lmms-lab/flickr30k"
md_url: "https://www.modelscope.cn/datasets/lmms-lab/flickr30k.md"
repository: lmms-lab/flickr30k
last_updated: 2024-11-21
license: "Apache License 2.0"
storage_size: "4.1 GB"
downloads: 5614
stars: 4
---

# flickr30k

> flickr30k - lmms-lab 在 ModelScope 开源的数据集。Large-scale Multi-modality Models Evaluation Suite

lmms-lab/flickr30k 是 ModelScope 魔搭社区上的数据集，存储大小 4.1 GB，采用 Apache License 2.0 许可。

- **Repository**: lmms-lab/flickr30k
- **License**: Apache License 2.0
- **Storage size**: 4.1 GB
- **Downloads**: 5614
- **Stars**: 4
- **Last updated**: 2024-11-21

Source: https://www.modelscope.cn/datasets/lmms-lab/flickr30k

---

<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png"  width="100%" height="80%">
</p>

# Large-scale Multi-modality Models Evaluation Suite

> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`

🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)

# This Dataset

This is a formatted version of [flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.

```
@article{young-etal-2014-image,
    title = "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions",
    author = "Young, Peter  and
      Lai, Alice  and
      Hodosh, Micah  and
      Hockenmaier, Julia",
    editor = "Lin, Dekang  and
      Collins, Michael  and
      Lee, Lillian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "2",
    year = "2014",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/Q14-1006",
    doi = "10.1162/tacl_a_00166",
    pages = "67--78",
    abstract = "We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.",
}
```