| # YFCC100M subset from OpenAI |
|
|
| Subset of [YFCC100M](https://arxiv.org/abs/1503.01817) used by OpenAI for [CLIP](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md), filtered to contain only the images that we were able to retrieve. |
|
|
| | Split | train | validation | |
| | --- | --- | --- | |
| | Number of samples | 14,808,859 | 16,374 | |
| | Size | 1.9 TB | 2.1 GB | |
|
|
| Features: |
| * from the original dataset: `title`, `description`, `photoid`, `uid`, `unickname`, `datetaken`, `dateuploaded`, `capturedevice`, `usertags`, `machinetags`, `longitude`, `latitude`, `accuracy`, `pageurl`, `downloadurl`, `licensename`, `licenseurl`, `serverid`, `farmid`, `secret`, `secretoriginal`, `ext`, `marker`, `key` |
| * `img`: image content, can be loaded with `PIL.Image.open(io.BytesIO(item['img']))` |
| * `title_clean` and `description_clean`: derived from `title` and `description` using `clean_text` function detailed below |
|
|
| ```python |
| def clean_text(text): |
| # decode url |
| text = urllib.parse.unquote_plus(text) |
| # remove html tags |
| text = re.sub('<[^<]+?>', '', text) |
| # remove multiple spaces + "\r" + "\n" + "\t" |
| text = " ".join(text.split()) |
| return text |
| ``` |
