| # Filtered WIT, an Image-Text Dataset. | |
| A reliable Dataset to run Image-Text models. | |
| You can find WIT, Wikipedia Image Text Dataset, [here](https://github.com/google-research-datasets/wit) | |
| Data was taken from [dalle-mini/wit](https://huggingface.co/datasets/dalle-mini/wit) | |
| ## Author | |
| - [Aarush Katta](https://github.com/ARKseal) | |
| ## Data Structure | |
| The data is stored as tars, containing 10,000 samples per tar. | |
| The parquets contain the metadata of each tar, which was crated using [this script](https://huggingface.co/datasets/laion/filtered-wit/blob/main/wit_create_meta.py) | |
| Each tar contains a `.jpg`, `.txt`, and `.json`. | |
| The image is stored in `.jpg`, the caption in `.txt.` and the metadata in `.json` | |
| The preferred method to read the data is [WebDataset](https://github.com/webdataset/webdataset) | |
| Here's an example: | |
| ```python | |
| import webdataset as wds | |
| dataset = wds.WebDataset('data/00000.tar').to_tuple('txt', 'jpg', 'json') | |
| for text, image, meta in dataset: | |
| print( | |
| text[:50], | |
| image[:50], | |
| meta[:50] | |
| ) | |
| ``` | |
| ## Filteration | |
| Each sample has 8 possible captions which were compared to the image using [CLIP ViT-B32](https://arxiv.org/abs/2103.00020) | |
| The text was encoded using [multilingual CLIP text encoder](https://huggingface.co/sentence-transformers/clip-ViT-B-32-multilingual-v1) | |
| Each possible caption was compared to the encoded image using Cosine Similarity | |
| and kept if the sim was greater than `0.26` | |
| Then the new caption was the filtered captions concatenated, and samples with no filtered caption were dropped. | |
| The script used is [filter_wit.py](https://huggingface.co/datasets/laion/filtered-wit/blob/main/filter_wit.py) | |