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---
license: cc
task_categories:
- image-to-text
task_ids:
- image-captioning
language:
- vi
size_categories:
- 100M<n<1B
pretty_name: Google WIT Vietnamese
---
# Google WIT Vietnamese
This data repos contain extracted data from [Google WIT](https://github.com/google-research-datasets/wit/blob/main/DATA.md). The extracted data is all for Vietnamese language.
Given `x` is a data point in the OG dataset which has keys following OG `field_name`, the criteria to filter is
```python
criteria = lambda x: x.get("language", "") == "vi" and x.get("caption_reference_description", "")
```
## Text-related details
All `.tsv.gz` files follow OG data files in terms of file names and file structures.
### Train split
`wit_v1.train.*.tsv.gz`
Train data length of each file (not including the header),
```
17690
17756
17810
17724
17619
17494
17624
17696
17777
17562
```
Total 176752
### Validation split
`wit_v1.val.*.tsv.gz`
Val data length of each file (not including the header),
```
292
273
275
320
306
```
Total 1466
### Test split
`wit_v1.test.*.tsv.gz`
Test data length of each file (not including the header),
```
215
202
201
201
229
```
Total 1048
## Image-related details
### Image URL only
`*.image_url_list.txt` are simply lists of image urls from `*.tsv.gz` files
Image url length of each file (train, val, test, all)
```
157281
1271
900
159452
```
Google Research has made sure that all sets don't share same exact images.
### Downloaded Images
⚠ Please for the love of the gods, read this section carefully.
For `all.index.fmt_id.image_url_list.tsv`, from left to right, without headers, the columns are `index`, `fmt_id`, `image_url`. It is to map `image_url` (in `all.image_url_list.txt`) to `fmt_id`. It's for downloading images.
`fmt_id` is:
- used to name images (with proper image extensions) in `images/`.
- `index` but filled with 6 zeros
Downloading time was less than 36 hours with:
- 90 Mbps
- Processor Intel(R) Core(TM) i7-8550U CPU @ 1.80GHz 1.99 GHz
- No asynchronous
For `fail.index.fmt_id.status.image_url_list.tsv`, from left to right, without headers, the columns are `index`, `fmt_id`, `status`, `image_url`. It is to track image urls (during downloading) that are inaccessible.
3367 image urls returned 404 (`status` values). In other words, we were able to download 97.88839275% of images.
`images/` folder takes disk space of:
- 215 GBs (uncompressed)
- 209 GBs (compressed)
We use Pillow to open each image to make sure that downloaded images are usable. We also log all faulty files in `corrupted_image_list.json`. There are less than 70 image files.
For `corrupted_image_list.json`, for each item in this list, the keys are `file_name`, `error`. `file_name` is `fmt_id` with extension but without `images/`. Some errors are either:
- files exceed Pillow default limit
- files are truncated
To actually load those files, the following code can be used to change Pillow behavior
```python
from PIL import Image, ImageFile
# For very big image files
Image.MAX_IMAGE_PIXELS = None
# For truncated image files
ImageFile.LOAD_TRUNCATED_IMAGES = True
```
Zip `images/` folder,
```bash
zip -r images.zip images/
zip images.zip --out spanned_images.zip -s 40g
```
https://superuser.com/questions/336219/how-do-i-split-a-zip-file-into-multiple-segments
Unzip `spanned_images.*` files,
```bash
zip -s 0 spanned_images.zip --out images.zip
unzip images.zip
```
https://unix.stackexchange.com/questions/40480/how-to-unzip-a-multipart-spanned-zip-on-linux |