|
--- |
|
license: apache-2.0 |
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configs: |
|
- config_name: default |
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data_files: |
|
- split: train |
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path: |
|
- train/EN.jsonl |
|
- train/ZH.jsonl |
|
- train/PT.jsonl |
|
- train/JA.jsonl |
|
- train/FR.jsonl |
|
- train/ES.jsonl |
|
- train/IT.jsonl |
|
- train/DE.jsonl |
|
- split: test |
|
path: |
|
- test/EN.jsonl |
|
- test/ZH.jsonl |
|
- test/PT.jsonl |
|
- test/JA.jsonl |
|
- test/FR.jsonl |
|
- test/ES.jsonl |
|
- test/IT.jsonl |
|
- test/DE.jsonl |
|
language: |
|
- en |
|
- zh |
|
- pt |
|
- ja |
|
- fr |
|
- es |
|
- it |
|
- de |
|
- ru |
|
- ar |
|
- ko |
|
- tr |
|
- th |
|
- hi |
|
task_categories: |
|
- translation |
|
- image-to-text |
|
size_categories: |
|
- 10M<n<100M |
|
--- |
|
|
|
## MIT-10M |
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|
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**Paper:** https://arxiv.org/abs/2412.07147 |
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|
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**Introduction:** |
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|
|
Image Translation (IT) holds immense potential across diverse domains, enabling the translation of textual content within images into various languages. |
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However, existing datasets often suffer from limitations in scale, diversity, and quality, hindering the development and evaluation of IT models. |
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To address this issue, we introduce MIT-10M, a large-scale parallel corpus of multilingual image translation with over 10M image-text pairs derived from real-world data, which has undergone extensive data cleaning and multilingual translation validation. |
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It contains 0.8M images in three sizes, 28 categories, tasks with three levels of difficulty and 14 languages image-text pairs, which is a considerable improvement on existing datasets. |
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|
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|
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### Citation Information |
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You can cite our paper https://arxiv.org/abs/2412.07147 |
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``` |
|
@misc{li2024mit10mlargescaleparallel, |
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title={MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation}, |
|
author={Bo, Li and Shaolin, Zhu and Lijie Wen}, |
|
year={2024}, |
|
eprint={2412.07147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV}, |
|
url={https://arxiv.org/abs/2412.07147}, |
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} |
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|
|
``` |
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|
