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---
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}
}
```
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