README.md

metadata

dataset_info:
  config_name: parquet
  features:
    - name: text
      dtype: string
    - name: id
      dtype: string
    - name: url
      dtype: string
    - name: caption
      dtype: string
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: mime_type
      dtype: string
    - name: hash
      dtype: string
    - name: license
      dtype: string
    - name: source
      dtype: string
  splits:
    - name: train
      num_bytes: 8655889565
      num_examples: 12249454
  download_size: 3647461171
  dataset_size: 8655889565
configs:
  - config_name: parquet
    data_files:
      - split: train
        path: parquet/train-*
task_categories:
  - question-answering
language:
  - en
  - tr
pretty_name: PD12M Turkish
size_categories:
  - 10M<n<100M
license: cdla-permissive-2.0

Translated from English to Tuskish language from: https://huggingface.co/datasets/Spawning/PD12M One of the biggest text-to-image dataset in Turkish language

Metadata

The metadata is made available through a series of parquet files with the following schema:

• text: Translated caption for the image.
• id: A unique identifier for the image.
• url: The URL of the image.
• caption: A caption for the image.
• width: The width of the image in pixels.
• height: The height of the image in pixels.
• mime_type: The MIME type of the image file.
• hash: The MD5 hash of the image file.
• license: The URL of the image license.
• source : The source organization of the image.

Download Images:

from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
    for _ in range(retries + 1):
        try:
            request = urllib.request.Request(
                image_url,
                data=None,
                headers={"user-agent": USER_AGENT},
            )
            with urllib.request.urlopen(request, timeout=timeout) as req:
                image = PIL.Image.open(io.BytesIO(req.read()))
            break
        except Exception:
            image = None
    return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
    fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
    with ThreadPoolExecutor(max_workers=num_threads) as executor:
        batch["image"] = list(executor.map(fetch_single_image_with_args, batch["url"]))
    return batch


num_threads = 20
dataset = dataset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})