---
license: cc-by-4.0
dataset_info:
  features:
  - name: SAMPLE_ID
    dtype: int64
  - name: URL
    dtype: string
  - name: TEXT
    dtype: string
  - name: HEIGHT
    dtype: float64
  - name: WIDTH
    dtype: float64
  - name: LICENSE
    dtype: string
  - name: NSFW
    dtype: string
  - name: similarity
    dtype: float64
  - name: ase_scores
    dtype: float64
  - name: kmeans
    dtype: int64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 28506248899
    num_examples: 107166507
  download_size: 16353125308
  dataset_size: 28506248899
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# 100M Text Debiased Subset from LAION 2B

- Captions in LAION-2B have a significant bias towards describing visual text content embedded in the images.
- Released CLIP models have strong text spotting bias in almost every style of web images, resulting in the CLIP-filtering datasets inherently biased towards visual text dominant data.
- CLIP models easily learn text spotting capacity from parrot captions while failing to connect the vision-language semantics, just like a text spotting parrot.

For more details, please see our [paper](https://arxiv.org/abs/2312.14232).

## Filtering Details

We provide an alternative solution by releasing a less biased filtered LAION-2B 100M(107,166,507) subset.

We construct a less biased 100M subset from the LAION-2B subset with Empty OCR results, CLIP score > 0.3, and Aesthetics score > 4.5.

We add the ase_scores and K-means labels (4000 total) for each image-text pair.

*We also released the dataset on [OpenDataLab](https://openxlab.org.cn/datasets/opendatalab-linyiqi/LAION-text-debiased-100M).*

The pre-trained CLIP model is released on [github](https://github.com/opendatalab/CLIP-Parrot-Bias).

## Reference
```
@article{lin2023parrot,
  title={Parrot Captions Teach CLIP to Spot Text}, 
  author={Yiqi Lin and Conghui He and Alex Jinpeng Wang and Bin Wang and Weijia Li and Mike Zheng Shou},
  journal={arXiv preprint arXiv:2312.14232},
  year={2023}
}
@misc{conghui2022opendatalab,
  author={He, Conghui and Li, Wei and Jin, Zhenjiang and Wang, Bin and Xu, Chao and Lin, Dahua},
  title={OpenDataLab: Empowering General Artificial Intelligence with Open Datasets},
  howpublished = {\url{https://opendatalab.com}},
  year={2022}
}
```