|
--- |
|
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 |
