README.md

---
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