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--- |
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language: |
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- en |
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license: mit |
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size_categories: |
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- 1M<n<10M |
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task_categories: |
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- visual-question-answering |
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- image-text-to-text |
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pretty_name: ABC-Pretraining-Data |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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dataset_info: |
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features: |
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- name: caption |
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dtype: string |
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- name: url |
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dtype: string |
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- name: id |
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dtype: int64 |
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- name: image |
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dtype: string |
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- name: negatives |
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sequence: int64 |
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splits: |
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- name: train |
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num_bytes: 2289772991 |
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num_examples: 2252041 |
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download_size: 1855548818 |
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dataset_size: 2289772991 |
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tags: |
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- visual |
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- multimodal |
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- vision-language-model |
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- retrieval |
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--- |
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## ABC Pretraining Data |
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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. |
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This dataset is derived from Google's [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/) dataset. |
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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. |
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**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). |
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## Paper, Project Page, and Code |
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- Paper: [ABC: Achieving Better Control of Multimodal Embeddings using VLMs](https://huggingface.co/papers/2503.00329) |
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- Project Page: [https://tiger-ai-lab.github.io/ABC/](https://tiger-ai-lab.github.io/ABC/) |
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- Code: [https://github.com/TIGER-AI-Lab/ABC](https://github.com/TIGER-AI-Lab/ABC) |
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## Sample Usage |
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### Quick Start |
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First, install the necessary dependencies by cloning the repository and installing requirements: |
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```bash |
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git clone https://github.com/TIGER-AI-Lab/ABC |
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cd ABC |
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pip install -r requirements.txt |
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``` |
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Then, you can start making multimodal embeddings: |
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```python |
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python -i ./quick_start.py |
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``` |
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### Fetching Datasets from 🤗 Hub |
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Our datasets are hosted on HuggingFace Hub. The text data and dataset metadata can be fetched using HF's `load_dataset` utility. |
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To fetch the images from our datasets, we provide scripts in the `fetch_datasets` directory. |
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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`). |
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Run `python ./fetch_datasets/pretrain.py` to get the pretraining dataset and `python ./fetch_datasets/instruct.py` to get the finetuning dataset, respectively. |
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## Citation |
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If you find any of our work helpful, please consider citing: |
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```bibtex |
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@misc{schneider2025abcachievingbettercontrol, |
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title={ABC: Achieving Better Control of Multimodal Embeddings using VLMs}, |
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author={Benjamin Schneider and Florian Kerschbaum and Wenhu Chen}, |
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year={2025}, |
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eprint={2503.00329}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2503.00329}, |
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} |
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``` |
