README.md

---
language:
- en
license: mit
size_categories:
- 1M<n<10M
task_categories:
- visual-question-answering
- image-text-to-text
pretty_name: ABC-Pretraining-Data
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: caption
    dtype: string
  - name: url
    dtype: string
  - name: id
    dtype: int64
  - name: image
    dtype: string
  - name: negatives
    sequence: int64
  splits:
  - name: train
    num_bytes: 2289772991
    num_examples: 2252041
  download_size: 1855548818
  dataset_size: 2289772991
tags:
- visual
- multimodal
- vision-language-model
- retrieval
---

## ABC Pretraining Data

This dataset contains the pretraining data for ABC, an open-source multimodal embedding model that uses a vision-language model backbone to deeply integrate image features with natural language instructions, advancing the state of visual embeddings with natural language control.

This dataset is derived from Google's [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/) dataset.
Each item in the dataset contains a URL where the corresponding image can be downloaded and mined negatives for each item. The full dataset is ~300 GB of images. For a detailed description of how we mined the negatives, please check out our paper.
**Update**: The images have been added to this repository. For an example of how to use and download this dataset, see our [repository](https://github.com/TIGER-AI-Lab/ABC).

## Paper, Project Page, and Code

- Paper: [ABC: Achieving Better Control of Multimodal Embeddings using VLMs](https://huggingface.co/papers/2503.00329)
- Project Page: [https://tiger-ai-lab.github.io/ABC/](https://tiger-ai-lab.github.io/ABC/)
- Code: [https://github.com/TIGER-AI-Lab/ABC](https://github.com/TIGER-AI-Lab/ABC)

## Sample Usage

### Quick Start
First, install the necessary dependencies by cloning the repository and installing requirements:
```bash
git clone https://github.com/TIGER-AI-Lab/ABC
cd ABC
pip install -r requirements.txt
```
Then, you can start making multimodal embeddings:
```python
python -i ./quick_start.py
```

### Fetching Datasets from 🤗 Hub
Our datasets are hosted on HuggingFace Hub. The text data and dataset metadata can be fetched using HF's `load_dataset` utility.
To fetch the images from our datasets, we provide scripts in the `fetch_datasets` directory.
These scripts will pull the pretraining/finetuning image data off the hub and unpack them in your huggingface datasets cache (under a directory called `tigerlab`).
Run `python ./fetch_datasets/pretrain.py` to get the pretraining dataset and `python ./fetch_datasets/instruct.py` to get the finetuning dataset, respectively.

## Citation

If you find any of our work helpful, please consider citing:

```bibtex
@misc{schneider2025abcachievingbettercontrol,
      title={ABC: Achieving Better Control of Multimodal Embeddings using VLMs},
      author={Benjamin Schneider and Florian Kerschbaum and Wenhu Chen},
      year={2025},
      eprint={2503.00329},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.00329},
}
```