metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: image_seg
      dtype: image
    - name: landmarks
      dtype: string
  splits:
    - name: train
      num_bytes: 33730885609
      num_examples: 100000
  download_size: 34096881533
  dataset_size: 33730885609

Dataset Card for `face_synthetics`

This is a copy of Microsoft FaceSynthetics dataset, uploaded to Hugging Face Datasets for convenience.

Please, refer to the original license, which we replicate in this repo.

The dataset was uploaded using the following code, which assumes the original zip file was uncompressed to /data/microsoft_face_synthetics:

from datasets import Dataset
from pathlib import Path
from PIL import Image

face_synthetics = Path("/data/microsoft_face_synthetics")

def entry_for_id(entry_id):
    if type(entry_id) == int:
        entry_id = f"{entry_id:06}"
    image = Image.open(face_synthetics/f"{entry_id}.png")
    image_seg = Image.open(face_synthetics/f"{entry_id}_seg.png")
    with open(face_synthetics/f"{entry_id}_ldmks.txt") as f:
        landmarks = f.read()
    return {
        "image": image,
        "image_seg": image_seg,
        "landmarks": landmarks,
    }

def generate_entries():
    for x in range(100000):
        yield entry_for_id(x)

ds = Dataset.from_generator(generate_entries)
ds.push_to_hub('pcuenq/face_synthetics')

Note that image_seg, the segmented images, appear to be black because each pixel contains a number between 0 to 18 corresponging to the different categories, see the original README for details. We haven't created visualization code yet.

Dataset Card for face_synthetics

Dataset Card for `face_synthetics`